Cross-View Consistency Regularisation for Knowledge Distillation

• URL: http://arxiv.org/abs/2412.16493v1
• Date: Sat, 21 Dec 2024 05:41:47 GMT
• Title: Cross-View Consistency Regularisation for Knowledge Distillation
• Authors: Weijia Zhang, Dongnan Liu, Weidong Cai, Chao Ma,
• Abstract summary: This work is inspired by the success of cross-view learning in fields such as semi-supervised learning. We introduce within-view and cross-view regularisations to standard logit-based distillation frameworks. We also perform confidence-based soft label mining to improve the quality of distilling signals from the teacher.
• Score: 13.918476599394603
• License:
• Abstract: Knowledge distillation (KD) is an established paradigm for transferring privileged knowledge from a cumbersome model to a lightweight and efficient one. In recent years, logit-based KD methods are quickly catching up in performance with their feature-based counterparts. However, previous research has pointed out that logit-based methods are still fundamentally limited by two major issues in their training process, namely overconfident teacher and confirmation bias. Inspired by the success of cross-view learning in fields such as semi-supervised learning, in this work we introduce within-view and cross-view regularisations to standard logit-based distillation frameworks to combat the above cruxes. We also perform confidence-based soft label mining to improve the quality of distilling signals from the teacher, which further mitigates the confirmation bias problem. Despite its apparent simplicity, the proposed Consistency-Regularisation-based Logit Distillation (CRLD) significantly boosts student learning, setting new state-of-the-art results on the standard CIFAR-100, Tiny-ImageNet, and ImageNet datasets across a diversity of teacher and student architectures, whilst introducing no extra network parameters. Orthogonal to on-going logit-based distillation research, our method enjoys excellent generalisation properties and, without bells and whistles, boosts the performance of various existing approaches by considerable margins.

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