On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?

• URL: http://arxiv.org/abs/2011.08121v1
• Date: Mon, 16 Nov 2020 17:34:55 GMT
• Title: On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?
• Authors: Yao-Chun Chan, Mingchen Li, Samet Oymak
• Abstract summary: We present a novel framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. Our experiments reveal two key insights: (i) Self-supervised pre-training significantly improves semi-supervised learning, especially in the few-label regime. We fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.
• Score: 31.563514432259897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-training, that enable superior utilization of unlabeled data which led to a significant reduction in required labeling in the standard machine learning benchmarks. A natural question is whether these paradigms can be unified to obtain superior results. To this aim, this paper provides a novel algorithmic framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. We conduct extensive experiments with our framework on CIFAR10 and CIFAR100 datasets. These experiments enable us to isolate and assess the benefits of individual components which are evaluated using state-of-the-art methods (e.g.~Core-Set, VAAL, simCLR, FixMatch). Our experiments reveal two key insights: (i) Self-supervised pre-training significantly improves semi-supervised learning, especially in the few-label regime, (ii) The benefit of active learning is undermined and subsumed by S4L techniques. Specifically, we fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.

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