From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning

• URL: http://arxiv.org/abs/2311.09974v2
• Date: Tue, 26 Mar 2024 08:04:00 GMT
• Title: From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning
• Authors: Jiansong Zhang, Linlin Shen, Peizhong Liu,
• Abstract summary: This paper proposes an adaptive technique of batch fusion for self-supervised contrastive learning. It achieves state-of-the-art performance under equitable comparisons. We suggest that the proposed method may contribute to the advancement of data-driven self-supervised learning research.
• Score: 32.18543787821028
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, self-supervised contrastive learning has emerged as a distinguished paradigm in the artificial intelligence landscape. It facilitates unsupervised feature learning through contrastive delineations at the instance level. However, crafting an effective self-supervised paradigm remains a pivotal challenge within this field. This paper delves into two crucial factors impacting self-supervised contrastive learning-bach size and pretext tasks, and from a data processing standpoint, proposes an adaptive technique of batch fusion. The proposed method, via dimensionality reduction and reconstruction of batch data, enables formerly isolated individual data to partake in intra-batch communication through the Embedding Layer. Moreover, it adaptively amplifies the self-supervised feature encoding capability as the training progresses. We conducted a linear classification test of this method based on the classic contrastive learning framework on ImageNet-1k. The empirical findings illustrate that our approach achieves state-of-the-art performance under equitable comparisons. Benefiting from its "plug-and-play" characteristics, we further explored other contrastive learning methods. On the ImageNet-100, compared to the original performance, the top1 has seen a maximum increase of 1.25%. We suggest that the proposed method may contribute to the advancement of data-driven self-supervised learning research, bringing a fresh perspective to this community.

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