Visual Transformer for Task-aware Active Learning

2021/06/07 by Razvan Caramalau, Binod Bhattarai, Caramalau, Razvan +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.2106.03801

openalex publication_date 2021/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Pool-based sampling in active learning (AL) represents a key framework for an-notating informative data when dealing with deep learning models. In this paper, we present a novel pipeline for pool-based Active Learning. Unlike most previous works, our method exploits accessible unlabelled examples during training to estimate their co-relation with the labelled examples. Another contribution of this paper is to adapt Visual Transformer as a sampler in the AL pipeline. Visual Transformer models non-local visual concept dependency between labelled and unlabelled examples, which is crucial to identifying the influencing unlabelled examples. Also, compared to existing methods where the learner and the sampler are trained in a multi-stage manner, we propose to train them in a task-aware jointly manner which enables transforming the latent space into two separate tasks: one that classifies the labelled examples; the other that distinguishes the labelling direction. We evaluated our work on four different challenging benchmarks of classification and detection tasks viz. CIFAR10, CIFAR100,FashionMNIST, RaFD, and Pascal VOC 2007. Our extensive empirical and qualitative evaluations demonstrate the superiority of our method compared to the existing methods. Code available: https://github.com/razvancaramalau/Visual-Transformer-for-Task-aware-Active-Learning

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• The Pascal Visual Object Classes (VOC) Challenge
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• Visual Transformers: Token-based Image Representation and Processing for Computer Vision
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise

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