Caramalau, R;
Bhattarai, B;
Stoyanov, D;
Kim, TK;
(2022)
MoBYv2AL: Self-supervised Active Learning for Image Classification.
In:
Proceedings of the 33rd British Machine Vision Conference Proceedings (BMVC 2022).
(pp. pp. 1-13).
The British Machine Vision Association
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | MoBYv2AL: Self-supervised Active Learning for Image Classification |
| Event: | BMVC 2022: 33rd British Machine Vision Conference |
| Location: | London, UK |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://bmvc2022.mpi-inf.mpg.de/674/ |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10180839 |
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