Generative replay with feedback connections as a general strategy for continual learning

2018/09/27 by Gido M. van de Ven, Andreas S. Tolias, van de Ven, Gido M. +1 · 20 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Multimodal Machine Learning Applications

paper · doi:10.48550/arxiv.1809.10635

openalex publication_date 2018/09/27 · openalex created_date 2018/10/05 · openalex updated_date 2026/07/28

Abstract

A major obstacle to developing artificial intelligence applications capable of true lifelong learning is that artificial neural networks quickly or catastrophically forget previously learned tasks when trained on a new one. Numerous methods for alleviating catastrophic forgetting are currently being proposed, but differences in evaluation protocols make it difficult to directly compare their performance. To enable more meaningful comparisons, here we identified three distinct scenarios for continual learning based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as "soft targets") achieved superior performance in all three scenarios. Addressing the issue of efficiency, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback or backward connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.

Citations

• Adam: A Method for Stochastic Optimization
• Distilling the Knowledge in a Neural Network
• Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions.
• An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
• Catastrophic Forgetting, Rehearsal and Pseudorehearsal
• Tutorial on Variational Autoencoders
• Learning without Forgetting
• PathNet: Evolution Channels Gradient Descent in Super Neural Networks
• A Strategy for an Uncompromising Incremental Learner

Cited by

• Learning continually with representational drift
• On the Dangers of Bootstrapping Generation for Continual Learning and Beyond
• Neuroscience-Inspired Memory Replay for Continual Learning: A Comparative Study of Predictive Coding and Backpropagation-Based Strategies
• Deep Generative Continual Learning using Functional LoRA: FunLoRA
• Avoid Catastrophic Forgetting with Rank-1 Fisher from Diffusion Models
• Class Incremental Continual Learning with Self-Organizing Maps and Variational Autoencoders Using Synthetic Replay
• Investigation of D-Wave quantum annealing for training Restricted Boltzmann Machines and mitigating catastrophic forgetting
• Recent Advances of Continual Learning in Computer Vision: An Overview
• Three scenarios for continual learning
• Continual Learning with Adaptive Weights (CLAW)
• Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
• Task Agnostic Continual Learning Using Online Variational Bayes
• Online Continual Learning in Image Classification: An Empirical Survey
• Posterior Meta-Replay for Continual Learning
• IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
• Leveraging Lightweight Generators for Memory Efficient Continual Learning
• Lifelong Object Detection
• Localizing Catastrophic Forgetting in Neural Networks
• Center Loss Regularization for Continual Learning
• Frugal Incremental Generative Modeling using Variational Autoencoders
• Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines

Related

• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
• Generative Feature Replay with Orthogonal Weight Modification for Continual Learning
• Generative Negative Replay for Continual Learning
• Three scenarios for continual learning
• Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization