MoBYv2AL: Self-supervised Active Learning for Image Classification

by   Razvan Caramalau, et al.

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. Code available:


page 4

page 7

page 8

page 14


Visual Transformer for Task-aware Active Learning

Pool-based sampling in active learning (AL) represents a key framework f...

Using Self-Supervised Pretext Tasks for Active Learning

Labeling a large set of data is expensive. Active learning aims to tackl...

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, superv...

Toward Realistic Evaluation of Deep Active Learning Algorithms in Image Classification

Active Learning (AL) aims to reduce the labeling burden by interactively...

Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning

Applying Machine learning to domains like Earth Sciences is impeded by t...

Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

With the rapid development of self-supervised learning (e.g., contrastiv...

Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI

The success of today's AI applications requires not only model training ...

Please sign up or login with your details

Forgot password? Click here to reset