Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

2017/08/25 by Han Xiao, Xiao, Han, Kashif Rasul +3 · 960 citations
Computer Science · Mathematics · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.CV #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1708.07747

Dataset is freely available at https://github.com/zalandoresearch/fashion-mnist Benchmark is available at http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/

openalex publication_date 2017/08/25 · arxiv created 2017/09/15 · arxiv updated 2017/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist

Citations

• Gradient-based learning applied to document recognition
• EMNIST: an extension of MNIST to handwritten letters

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• Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage
• Energy-Inspired Models: Learning with Sampler-Induced Distributions
• MAC: An Efficient Gradient Preconditioning using Mean Activation Approximated Curvature
• Coalesced Multi-Output Tsetlin Machines with Clause Sharing
• An Adaptive Method Stabilizing Activations for Enhanced Generalization
• A Close Look at Deep Learning with Small Data
• iCVI-ARTMAP: Accelerating and improving clustering using adaptive resonance theory predictive mapping and incremental cluster validity indices
• On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator
• Being a Bit Frequentist Improves Bayesian Neural Networks
• Hierarchical VAEs Know What They Don't Know
• Task Agnostic Continual Learning Using Online Variational Bayes
• Regularizing Neural Networks via Stochastic Branch Layers
• Heuristic Rank Selection with Progressively Searching Tensor Ring Network
• Deep Active Learning with Augmentation-based Consistency Estimation
• Pruned Neural Networks are Surprisingly Modular
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
• A Topological Improvement of the Overall Performance of Sparse Evolutionary Training: Motif-Based Structural Optimization of Sparse MLPs Project
• Mixture of Experts in Large Language Models
• Instance-dependent Label-noise Learning under a Structural Causal Model
• Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization
• Variational Conditional GAN for Fine-grained Controllable Image Generation
• Pixel-wise Conditioning of Generative Adversarial Networks
• Flows and Diffusions on the Neural Manifold
• Label Noise Types and Their Effects on Deep Learning
• Disturbing Target Values for Neural Network Regularization
• DaiMoN: A Decentralized Artificial Intelligence Model Network
• Incomplete Multiview Learning via Wyner Common Information
• Learning Invariant Representation for Continual Learning
• NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment
• Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
• Data Depth as a Risk
• SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
• CoreSPECT: Enhancing Clustering Algorithms via an Interplay of Density and Geometry
• A framework for the extraction of Deep Neural Networks by leveraging public data
• Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker
• Weight-Space Mixture-of-Experts for Implicit Neural Representation Classification
• A Hybrid Multilayer Extreme Learning Machine for Image Classification with an Application to Quadcopters
• Effective Version Space Reduction for Convolutional Neural Networks
• AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing
• Feedback Control for Online Training of Neural Networks
• Unreduced Persistence Diagrams for Topological Machine Learning
• More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data
• AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
• Energy-Efficient Supervised Learning with a Binary Stochastic Forward-Forward Algorithm
• A Method for Optimizing Connections in Differentiable Logic Gate Networks
• An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification
• Efficient Federated Learning with Timely Update Dissemination
• A Theoretical Framework for Target Propagation
• Unsupervised Out-of-Distribution Detection with Batch Normalization
• Semi-supervised learning by selective training with pseudo labels via confidence estimation
• QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
• Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization
• Label differential privacy via clustering
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment
• Encoding the Euler Characteristic Transform
• Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra
• Effort-Optimized, Accuracy-Driven Labelling and Validation of Test Inputs for DL Systems: A Mixed-Integer Linear Programming Approach
• Generating Unrestricted Adversarial Examples via Three Parameters
• CMET: Clustering guided METric for quantifying embedding quality
• Newton Method for Fixed-Support Doubly Entropic Wasserstein Barycenter
• TiFL: A Tier-based Federated Learning System
• Soft-Root-Sign Activation Function
• Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data
• StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers
• Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need
• Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data
• Tractable Representation Learning with Probabilistic Circuits
• Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
• Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
• Annealing Genetic GAN for Minority Oversampling
• On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
• BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization
• In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor
• Weight-Covariance Alignment for Adversarially Robust Neural Networks
• PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
• Generative Adversarial Networks with Inverse Transformation Unit
• Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation
• How Does Mixup Help With Robustness and Generalization?
• Training Generative Adversarial Networks with Adaptive Composite Gradient
• Variational Kolmogorov-Arnold Network
• Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment
• Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
• GradMetaNet: An Equivariant Architecture for Learning on Gradients
• Adaptive Sampling for Minimax Fair Classification
• Deep Transformation-Invariant Clustering
• Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
• Bit Error Robustness for Energy-Efficient DNN Accelerators
• Diffusion Disambiguation Models for Partial Label Learning
• Privacy-preserving Collaborative Learning with Automatic Transformation Search
• Multi-Task Variational Information Bottleneck
• BooVAE: Boosting Approach for Continual Learning of VAE
• Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
• Neural Langevin Machine: a local asymmetric learning rule can be creative
• Capsule Routing via Variational Bayes
• Hyperparameter-Free Out-of-Distribution Detection Using Softmax of Scaled Cosine Similarity
• Online Normalization for Training Neural Networks
• Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
• L_DMI: An Information-theoretic Noise-robust Loss Function
• Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection
• CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
• Improving Convergence for Semi-Federated Learning: An Energy-Efficient Approach by Manipulating Over-the-Air Distortion
• Rao-Blackwellised Reparameterisation Gradients
• ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
• Federated Learning with Fair Averaging
• Linearity-based neural network compression
• FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning
• Learning Translation Invariance in CNNs
• Stein Latent Optimization for Generative Adversarial Networks
• Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models
• Limited Gradient Descent: Learning With Noisy Labels
• How many winning tickets are there in one DNN?
• Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
• Weakly Supervised Object Segmentation by Background Conditional Divergence
• The duality structure gradient descent algorithm: analysis and\n applications to neural networks
• Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks
• Enhanced Image Recognition Using Gaussian Boson Sampling
• ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping
• Leveraging Lightweight Generators for Memory Efficient Continual Learning
• Iterative Quantum Feature Maps
• A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning
• Learning from Positive and Unlabeled Data with Arbitrary Positive Shift
• The Effect of Depth on the Expressivity of Deep Linear State-Space Models
• Vision-QRWKV: Exploring Quantum-Enhanced RWKV Models for Image Classification
• Focus Your Attention: Towards Data-Intuitive Lightweight Vision Transformers
• Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning
• Overcoming catastrophic forgetting with hard attention to the task
• Sharpness-Aware Minimization for Efficiently Improving Generalization
• Data Classification with Dynamically Growing and Shrinking Neural Networks
• DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation
• Revealing Quantum Information Encoded in Classical Images
• Toward Understanding Catastrophic Forgetting in Continual Learning
• EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting
• Virtual Conditional Generative Adversarial Networks
• Incentivizing High-quality Participation From Federated Learning Agents
• Transition of AI Models in dependence of noise
• DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligence
• Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding
• FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
• Time-dependent density estimation using binary classifiers
• PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
• Sequential Policy Gradient for Adaptive Hyperparameter Optimization
• Creating User-steerable Projections with Interactive Semantic Mapping
• Semi-Anchored Multi-Step Gradient Descent Ascent Method for Structured Nonconvex-Nonconcave Composite Minimax Problems
• DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
• Combining Model and Parameter Uncertainty in Bayesian Neural Networks
• Direct tensor processing with coherent light
• Widely Linear Kernels for Complex-Valued Kernel Activation Functions
• A Probabilistic Representation of Deep Learning for Improving The Information Theoretic Interpretability
• Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
• AugmentGest: Can Random Data Cropping Augmentation Boost Gesture Recognition Performance?
• Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation
• Visual Transformer for Task-aware Active Learning
• Overcoming Catastrophic Forgetting by Generative Regularization
• ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
• AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning
• PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering
• Clustering with UMAP: Why and How Connectivity Matters
• When Forgetting Triggers Backdoors: A Clean Unlearning Attack
• Nonlinear classifiers for ranking problems based on kernelized SVM
• Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates
• Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
• Entropic Issues in Likelihood-Based OOD Detection
• Characterizing the Decision Boundary of Deep Neural Networks
• Graph Semi-Supervised Learning for Point Classification on Data Manifolds
• Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification
• Multi-objective Evolutionary Approach for Efficient Kernel Size and Shape for CNN
• Mapping and Scheduling Spiking Neural Networks On Segmented Ladder Bus Architectures
• Probably Approximately Correct Constrained Learning
• Saturation Self-Organizing Map
• Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification
• QSEA: Quantum Self-supervised Learning with Entanglement Augmentation
• Sequential Off-Policy Learning with Logarithmic Smoothing
• Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack
• On the Intrinsic Differential Privacy of Bagging
• Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning
• HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification
• FMix: Enhancing Mixed Sample Data Augmentation
• Neural Complexity Measures
• Understanding the impact of entropy on policy optimization
• Distribution Estimation to Automate Transformation Policies for Self-Supervision
• Quantum optical reservoir computing powered by boson sampling
• Geometric Disentanglement by Random Convex Polytopes
• GeoClip: Geometry-Aware Clipping for Differentially Private SGD
• Infinity Search: Approximate Vector Search with Projections on q-Metric Spaces
• Poisoning Attacks with Generative Adversarial Nets
• Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
• Continual Classification Learning Using Generative Models
• Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
• How many degrees of freedom do we need to train deep networks: a loss landscape perspective
• Sparse Autoencoders, Again?
• DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
• Towards Reasonable Concept Bottleneck Models
• TabFlex: Scaling Tabular Learning to Millions with Linear Attention
• Aggregating explanation methods for stable and robust explainability
• Differentially Private Learning Needs Better Features (or Much More Data)
• Direct Optimization through arg max for Discrete Variational Auto-Encoder
• Overhead MNIST: A Benchmark Satellite Dataset
• Communication-Efficient Federated Learning with Compensated Overlap-FedAvg
• Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
• Maximal Jacobian-based Saliency Map Attack
• Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change
• Deep machine learning for meteor monitoring: Advances with transfer learning and gradient-weighted class activation mapping
• Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization
• Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks
• A decentralized asynchronous federated learning framework for edge devices
• Learning Representations For Images With Hierarchical Labels
• Uniform Interpolation Constrained Geodesic Learning on Data Manifold
• Variance Regularization for Accelerating Stochastic Optimization
• Amortised Learning by Wake-Sleep
• Federated Learning via Plurality Vote
• Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units
• Izhikevich-Inspired Temporal Dynamics for Enhancing Privacy, Efficiency, and Transferability in Spiking Neural Networks
• Kilobyte Models: Neural Networks as a Seed and a Quantized Latent
• Clustering-Oriented Representation Learning with Attractive-Repulsive\n Loss
• Federated Learning in Adversarial Settings
• Finite Versus Infinite Neural Networks: an Empirical Study
• Real-Time Decentralized knowledge Transfer at the Edge
• Learning an Adaptive Learning Rate Schedule
• Invertible Manifold Learning for Dimension Reduction
• Virus-MNIST: A Benchmark Malware Dataset
• Consensus Driven Learning
• PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
• Generalized Negative Correlation Learning for Deep Ensembling
• Adversarial Examples in Modern Machine Learning: A Review
• Active Generative Adversarial Network for Image Classification
• StackNet: Stacking Parameters for Continual learning
• DNNs as Layers of Cooperating Classifiers
• eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges
• Bayesian Inference Forgetting
• Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
• Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
• Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization
• Learning a Probabilistic Strategy for Computational Imaging Sensor Selection
• Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond
• MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders
• Fundamental tenis : resep meraih kemenangan / Tony Mottram
• Towards More Efficient Federated Learning with Better Optimization Objects
• Decentralized Bayesian Learning over Graphs
• The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search
• GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure
• Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation
• Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
• Connecting Neural Models Latent Geometries with Relative Geodesic Representations
• When Optimizing f-divergence is Robust with Label Noise
• ρ-VAE: Autoregressive parametrization of the VAE encoder
• EWGN: Elastic Weight Generation and Context Switching in Deep Learning
• Enhancing Parallelism in Decentralized Stochastic Convex Optimization
• PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters
• Weight-Space Linear Recurrent Neural Networks
• Unified Adversarial Invariance
• Towards Robust Evaluations of Continual Learning
• Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset
• Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
• Seeing the Abstract: Translating the Abstract Language for Vision Language Models
• Learning from a Teacher using Unlabeled Data
• An Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations
• Optimized Local Updates in Federated Learning via Reinforcement Learning
• Typical Machine Learning Datasets as Low-Depth Quantum Circuits
• The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
• Cartan Networks: Group theoretical Hyperbolic Deep Learning
• Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
• Clusterability in Neural Networks
• Identifying Classes Susceptible to Adversarial Attacks
• Hyperbolic Dataset Distillation
• Explainable Deep One-Class Classification
• GARLIC: GAussian Representation LearnIng for spaCe partitioning
• Network Inversion for Uncertainty-Aware Out-of-Distribution Detection
• Navigating the Accuracy-Size Trade-Off with Flexible Model Merging
• Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning Dynamics
• Noisy HQNNs: A Comprehensive Analysis of Noise Robustness in Hybrid Quantum Neural Networks
• The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
• How to Evaluate Participant Contributions in Decentralized Federated Learning
• Shared and Private VAEs with Generative Replay for Continual Learning
• Differential Gated Self-Attention
• Bidirectional predictive coding
• Rectangular Flows for Manifold Learning
• Towards Understanding the Transferability of Deep Representations
• Uncertainty Quantification with Proper Scoring Rules: Adjusting Measures to Prediction Tasks
• ChatPD: An LLM-driven Paper-Dataset Networking System
• Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
• Credal Prediction based on Relative Likelihood
• Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
• Bayesian Robust Aggregation for Federated Learning
• Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detection
• Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
• Progressive Augmentation of GANs
• Feature Space Singularity for Out-of-Distribution Detection
• Practical estimation of the optimal classification error with soft labels and calibration
• Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis
• Evidential Deep Active Learning for Semi-Supervised Classification
• Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
• Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
• Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
• Incentivizing Inclusive Contributions in Model Sharing Markets
• Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments
• Kuramoto-FedAvg: Using Synchronization Dynamics to Improve Federated Learning Optimization under Statistical Heterogeneity
• Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer
• Probabilistic Autoencoder
• Meta-Learning Bidirectional Update Rules
• ePC: Fast and Deep Predictive Coding in Digital Simulation
• On the Robustness of Average Losses for Partial-Label Learning
• Adversarial Attack and Defense in Deep Ranking
• Corella: A Private Multi Server Learning Approach based on Correlated Queries
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier
• Learning from M-Tuple Dominant Positive and Unlabeled Data
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
• LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning
• Using Dimensionality Reduction to Optimize t-SNE
• Teaching a GAN What Not to Learn
• Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks
• Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimization
• Predictive Uncertainty Quantification with Compound Density Networks
• Learning Optimal Conformal Classifiers
• SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training
• Single Snapshot Distillation for Phase Coded Mask Design in Phase Retrieval
• Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior
• Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning
• Enabling Inference Privacy with Adaptive Noise Injection
• Robustness for Non-Parametric Classification: A Generic Attack and Defense
• ImmuNeCS: Neural Committee Search by an Artificial Immune System
• Task-Aware Variational Adversarial Active Learning
• PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons
• Adversarial Learning of General Transformations for Data Augmentation
• Constrained Co-Design for Photonic Bayesian Neural Networks
• Training on Plausible Counterfactuals Removes Spurious Correlations
• Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments
• ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning
• Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
• An ETF view of Dropout regularization
• Bayesian Sparsification Methods for Deep Complex-valued Networks
• Robust Variational Autoencoder
• Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
• Using a thousand optimization tasks to learn hyperparameter search strategies
• Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques
• Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
• Bidirectional Variational Autoencoders
• Distributionally Robust Federated Learning with Client Drift Minimization
• Last Layer Empirical Bayes
• Lightweight machine unlearning in neural network
• Large Language Models Implicitly Learn to See and Hear Just By Reading
• Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
• Adversarially Pretrained Transformers may be Universally Robust In-Context Learners
• Evolving parametrized Loss for Image Classification Learning on Small Datasets
• A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
• SifterNet: A Generalized and Model-Agnostic Trigger Purification Approach
• Puzzle-AE: Novelty Detection in Images through Solving Puzzles
• Shape Defense Against Adversarial Attacks
• Adaptive Regularization via Residual Smoothing in Deep Learning Optimization
• Anomaly Detection Based on Critical Paths for Deep Neural Networks
• Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses
• Enhancing Interpretability of Sparse Latent Representations with Class Information
• Cluster Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification
• Adversarial Detection and Correction by Matching Prediction Distributions
• Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy
• Spiking Neural Networks with Random Network Architecture
• Contrastive Learning for Lifted Networks
• Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision
• An Adaptive Empirical Bayesian Method for Sparse Deep Learning
• OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction
• Efficient training for large-scale optical neural network using an evolutionary strategy and attention pruning
• Improve SGD Training via Aligning Mini-batches
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
• Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression
• Traceable Black-box Watermarks for Federated Learning
• Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning
• Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networks
• Deformation Robust Roto-Scale-Translation Equivariant CNNs
• Overhead-MNIST: Machine Learning Baselines for Image Classification
• Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization
• SAFE-Merge: Data-Free Continual Model Merging with General Knowledge Preservation
• Fixed Point Explainability
• DeepOBS: A Deep Learning Optimizer Benchmark Suite
• γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning
• MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
• Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
• A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
• Sparse Techniques for Regression in Deep Gaussian Processes
• Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines
• Understanding the Effect of Bias in Deep Anomaly Detection
• On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
• Mitigating Sampling Bias and Improving Robustness in Active Learning
• Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios
• Decentralized Learning of Generative Adversarial Networks from Non-iid Data
• Dropout with Tabu Strategy for Regularizing Deep Neural Networks
• Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model
• Topology-Aware Knowledge Propagation in Decentralized Learning
• CascadeLUT: Information-Ordered Streaming Inference for Bandwidth-Constrained FPGAs
• Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
• Multiclass threshold-based classification
• RanDeS: Randomized Delta Superposition for Multi-Model Compression
• Minimax learning rates for estimating binary classifiers under margin conditions
• An Improved Evaluation Framework for Generative Adversarial Networks
• Multitask Learning with Single Gradient Step Update for Task Balancing
• AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
• Sybil-based Virtual Data Poisoning Attacks in Federated Learning
• Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network
• Task Guided Compositional Representation Learning for ZDA
• Energy-Efficient Federated Learning for AIoT using Clustering Methods
• Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
• Training Deep Morphological Neural Networks as Universal Approximators
• Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
• Network-Density-Controlled Decentralized Parallel Stochastic Gradient Descent in Wireless Systems
• PrePrompt: Predictive prompting for class incremental learning
• Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning
• DPER: Efficient Parameter Estimation for Randomly Missing Data
• Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
• Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
• Orthogonal-Padé Activation Functions: Trainable Activation functions for smooth and faster convergence in deep networks
• Incomplete In-context Learning
• Skeptical Deep Learning with Distribution Correction
• PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
• Dyn-D²P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
• FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
• Pushing the Limits of Capsule Networks
• Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
• Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security
• Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
• Regularization Shortcomings for Continual Learning
• DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information
• A Neural Scaling Law from the Dimension of the Data Manifold
• A Neuro-Symbolic Framework for Sequence Classification with Relational and Temporal Knowledge
• Improving the convergence of SGD through adaptive batch sizes
• DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
• Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation
• Incremental Class Learning using Variational Autoencoders with Similarity Learning
• Defining Benchmarks for Continual Few-Shot Learning
• Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
• NLNL: Negative Learning for Noisy Labels
• XNAS: Neural Architecture Search with Expert Advice
• How to Train an Oscillator Ising Machine using Equilibrium Propagation
• Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization
• Epistemic Wrapping for Uncertainty Quantification
• Towards Trustworthy Federated Learning with Untrusted Participants
• Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
• SpinalNet: Deep Neural Network With Gradual Input
• Dendritic Computing with Multi-Gate Ferroelectric Field-Effect Transistors
• Genealogical Population-Based Training for Hyperparameter Optimization
• CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
• Embedded hyper-parameter tuning by Simulated Annealing
• TActiLE: Tiny Active LEarning for wearable devices
• Quantum Support Vector Regression for Robust Anomaly Detection
• One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection
• A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture
• Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
• Measuring Information Transfer in Neural Networks
• Tensor Normalization and Full Distribution Training
• Measuring and Harnessing Transference in Multi-Task Learning
• Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)
• PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search
• Federated Continual Learning with Weighted Inter-client Transfer
• Coded-InvNet for Resilient Prediction Serving Systems
• Image classification via a quantum-inspired strategy involving a mixture of experts
• Generalised Perceptron Learning
• Topological Insights into Sparse Neural Networks
• McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
• KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks
• Dendritic Self-Organizing Maps for Continual Learning
• Prior-Independent Auctions for the Demand Side of Federated Learning
• CheckNet: Secure Inference on Untrusted Devices
• HOMRS: High Order Metamorphic Relations Selector for Deep Neural Networks
• Structured Stochastic Gradient MCMC
• Channel-Directed Gradients for Optimization of Convolutional Neural Networks
• MIM: Mutual Information Machine
• Learning Inward Scaled Hypersphere Embedding: Exploring Projections in\n Higher Dimensions
• FoCL: Feature-Oriented Continual Learning for Generative Models
• SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
• Self-Paced Learning with Adaptive Deep Visual Embeddings
• Learning the ARTS of Search for Automated Discovery
• Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
• MTL2L: A Context Aware Neural Optimiser
• MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
• Capsule Networks with Max-Min Normalization
• Croissant: A Metadata Format for ML-Ready Datasets
• Affinity guided Geometric Semi-Supervised Metric Learning
• Frustratingly Easy Transferability Estimation
• BUZz: BUffer Zones for defending adversarial examples in image classification
• STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
• AQR-HNSW: Accelerating Approximate Nearest Neighbor Search via Density-aware Quantization and Multi-stage Re-ranking
• Celo2: Towards Learned Optimization Free Lunch
• Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise
• Stochastic Generalized Adversarial Label Learning
• Statistically Undetectable Backdoors in Deep Neural Networks
• Zero-Flow Encoders
• Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning
• Subject Information Extraction for Novelty Detection with Domain Shifts
• Density-Aware Noise Mechanisms for Differential Privacy on Riemannian Manifolds via Conformal Transformation
• Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods
• Predicting integers from continuous parameters
• Faster Predictive Coding Networks via Better Initialization
• Learning Brenier Potentials with Convex Generative Adversarial Neural Networks
• Dynamic Tsetlin Machine Accelerators for On-Chip Training at the Edge using FPGAs
• Bayesian Quantum Orthogonal Neural Networks for Anomaly Detection
• QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
• Signature-Graph Networks
• Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning
• Multi-class Probabilistic Bounds for Self-learning
• Sparse Communication via Mixed Distributions
• Benchmarking Quantized Neural Networks on FPGAs with FINN
• Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
• Semi-Supervised Class Discovery
• On the generalization of bayesian deep nets for multi-class classification
• DOODLER: Determining Out-Of-Distribution Likelihood from Encoder Reconstructions
• Interpretable BoW Networks for Adversarial Example Detection
• Neuron Merging: Compensating for Pruned Neurons
• DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsules
• Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
• Measuring the Complexity of Domains Used to Evaluate AI Systems
• Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
• NASCaps
• Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples
• Architectural Resilience to Foreground-and-Background Adversarial Noise
• AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
• On Intrinsic Dataset Properties for Adversarial Machine Learning
• Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks
• A temporally and spatially local spike-based backpropagation algorithm to enable training in hardware
• Lifelong Learning Process: Self-Memory Supervising and Dynamically Growing Networks
• Quantum superposition inspired spiking neural network
• Noise-Tolerant Coreset-Based Class Incremental Continual Learning
• CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning
• Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
• MASS: MoErging through Adaptive Subspace Selection
• On Batch Normalisation for Approximate Bayesian Inference
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
• Quantum Doubly Stochastic Transformers
• VeLU: Variance-enhanced Learning Unit for Deep Neural Networks
• A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
• Robust Compressive Phase Retrieval via Deep Generative Priors
• DMPCN: Dynamic Modulated Predictive Coding Network with Hybrid Feedback Representations
• Structured and Unstructured Outlier Identification for Robust PCA: A Fast Parameter Free Algorithm
• Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
• Zero-Shot Deep Domain Adaptation
• Feature Selection for Huge Data via Minipatch Learning
• Learning from Reasoning Failures via Synthetic Data Generation
• Practical Deep Learning Architecture Optimization
• EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
• Bayesian continual learning and forgetting in neural networks
• Implicit Generative Copulas
• Bayesian Neural Network Priors Revisited
• Targeted Deep Learning: Framework, Methods, and Applications
• Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks
• Scalable Robust Bayesian Co-Clustering with Compositional ELBOs
• Exploring Layerwise Decision Making in DNNs
• Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible
• RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
• Generating Adversarial Examples with an Optimized Quality
• How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
• Convergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks
• Marginal Matching Does Not License Factorized Sampling: Auditing Conditional Style Leakage in Factorized Generative Models
• DG-FedReuse: Proxy-Gradient-Gated Cached-Update Reuse with Matched Sparse Uplink Accounting
• FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption
• QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning
• Learning with Spike Synchrony in Spiking Neural Networks
• The Impact of Model Zoo Size and Composition on Weight Space Learning
• SPreV
• Mixture of Group Experts for Learning Invariant Representations
• Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery
• Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
• pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
• Automating quantum feature map design via large language models
• Compound and Parallel Modes of Tropical Convolutional Neural Networks
• Binary Tree Block Encoding of Classical Matrix
• FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retraining
• Conditioning Diffusions Using Malliavin Calculus
• PPFPL: Cross-silo Privacy-preserving Federated Prototype Learning Against Data Poisoning Attacks

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• MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
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• Fashion-Gen: The Generative Fashion Dataset and Challenge