Visual Transformer for Task-aware Active Learning
This work addresses the challenge of efficiently annotating data for deep learning models, offering a task-aware joint training approach that improves over existing methods, though it appears incremental in its adaptation of transformers to active learning.
The paper tackles the problem of pool-based active learning by proposing a novel pipeline that uses a Visual Transformer to model dependencies between labeled and unlabeled examples, achieving superior performance on classification and detection benchmarks like CIFAR10, CIFAR100, FashionMNIST, RaFD, and Pascal VOC 2007.
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