Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning

• URL: http://arxiv.org/abs/2306.05101v2
• Date: Fri, 7 Jun 2024 05:06:48 GMT
• Title: Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
• Authors: Sungmin Cha, Kyunghyun Cho, Taesup Moon,
• Abstract summary: We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL) Our PNR leverages pseudo-negatives obtained through model-based augmentation in a way that newly learned representations may not contradict what has been learned in the past.
• Score: 62.40718385934608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL). Our PNR leverages pseudo-negatives obtained through model-based augmentation in a way that newly learned representations may not contradict what has been learned in the past. Specifically, for the InfoNCE-based contrastive learning methods, we define symmetric pseudo-negatives obtained from current and previous models and use them in both main and regularization loss terms. Furthermore, we extend this idea to non-contrastive learning methods which do not inherently rely on negatives. For these methods, a pseudo-negative is defined as the output from the previous model for a differently augmented version of the anchor sample and is asymmetrically applied to the regularization term. Extensive experimental results demonstrate that our PNR framework achieves state-of-the-art performance in representation learning during CSSL by effectively balancing the trade-off between plasticity and stability.

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