Intra-class Patch Swap for Self-Distillation

• URL: http://arxiv.org/abs/2505.14124v1
• Date: Tue, 20 May 2025 09:30:19 GMT
• Title: Intra-class Patch Swap for Self-Distillation
• Authors: Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga,
• Abstract summary: We propose a teacher-free distillation framework based on a single student network.<n>Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation.<n>Our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches.
• Score: 3.282914142012984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge distillation (KD) is a valuable technique for compressing large deep learning models into smaller, edge-suitable networks. However, conventional KD frameworks rely on pre-trained high-capacity teacher networks, which introduce significant challenges such as increased memory/storage requirements, additional training costs, and ambiguity in selecting an appropriate teacher for a given student model. Although a teacher-free distillation (self-distillation) has emerged as a promising alternative, many existing approaches still rely on architectural modifications or complex training procedures, which limit their generality and efficiency. To address these limitations, we propose a novel framework based on teacher-free distillation that operates using a single student network without any auxiliary components, architectural modifications, or additional learnable parameters. Our approach is built on a simple yet highly effective augmentation, called intra-class patch swap augmentation. This augmentation simulates a teacher-student dynamic within a single model by generating pairs of intra-class samples with varying confidence levels, and then applying instance-to-instance distillation to align their predictive distributions. Our method is conceptually simple, model-agnostic, and easy to implement, requiring only a single augmentation function. Extensive experiments across image classification, semantic segmentation, and object detection show that our method consistently outperforms both existing self-distillation baselines and conventional teacher-based KD approaches. These results suggest that the success of self-distillation could hinge on the design of the augmentation itself. Our codes are available at https://github.com/hchoi71/Intra-class-Patch-Swap.

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