MoBYv2AL: Self-supervised Active Learning for Image Classification

Razvan Caramalau (Imperial College),* Binod Bhattarai (University College London), Danail Stoyanov (UCL), Tae-Kyun Kim (Imperial College London)
The 33rd British Machine Vision Conference


Active learning(AL) has recently gained popularity for deep learning(DL) models. This is due to efficient and informative sampling, especially when the learner requires large-scale labelled datasets. Commonly, the sampling and training happen in stages while more batches are added. One main bottleneck in this strategy is the narrow representation learned by the model that affects the overall AL selection. We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY -- one of the most successful self-supervised learning algorithms to the AL pipeline. Thus, we add the downstream task-aware objective function and optimize it jointly with contrastive loss. Further, we derive a data-distribution selection function from labelling the new examples. Finally, we test and study our pipeline robustness and performance for image classification tasks. We successfully achieved state-of-the-art results when compared to recent AL methods.



author    = {Razvan Caramalau and Binod Bhattarai and Danail Stoyanov and Tae-Kyun Kim},
title     = {MoBYv2AL: Self-supervised Active Learning for Image Classification},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

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