Relational Representation Distillation (2407.12073v3)

Abstract: Knowledge distillation (KD) is an effective method for transferring knowledge from a large, well-trained teacher model to a smaller, more efficient student model. Despite its success, one of the main challenges in KD is ensuring the efficient transfer of complex knowledge while maintaining the student's computational efficiency. Unlike previous works that applied contrastive objectives promoting explicit negative instances with little attention to the relationships between them, we introduce Relational Representation Distillation (RRD). Our approach leverages pairwise similarities to explore and reinforce the relationships between the teacher and student models. Inspired by self-supervised learning principles, it uses a relaxed contrastive loss that focuses on similarity rather than exact replication. This method aligns the output distributions of teacher samples in a large memory buffer, improving the robustness and performance of the student model without the need for strict negative instance differentiation. Our approach demonstrates superior performance on CIFAR-100 and ImageNet ILSVRC-2012, outperforming traditional KD and sometimes even outperforms the teacher network when combined with KD. It also transfers successfully to other datasets like Tiny ImageNet and STL-10. Code is available at https://github.com/giakoumoglou/distillers.

The paper "Relational Representation Distillation" explores an innovative approach to knowledge distillation, which is a technique for transferring knowledge from a larger, well-trained teacher model to a smaller, more efficient student model. Knowledge distillation is crucial for deploying complex models in resource-constrained environments, but it faces challenges in efficiently transferring complex knowledge while maintaining the student model's computational efficiency.

Key Concepts

• Knowledge Distillation (KD): Traditionally involves aligning the output between teacher and student models, often using contrastive objectives that emphasize explicit negative instances.
• Relational Representation Distillation (RRD): The novel method introduced in the paper. It leverages pairwise similarities and focuses on exploring the relationships between teacher and student models rather than strictly differentiating negative instances.

Methodology

RRD is inspired by self-supervised learning principles and employs a relaxed contrastive loss. This approach emphasizes the similarity between model outputs, rather than requiring exact replication of the teacher's output. By using a large memory buffer, RRD aligns the output distributions of teacher samples, which enhances both robustness and performance of the student model. This method contrasts with traditional KD techniques by reducing the need for exact negative sampling.

Performance and Results

The authors demonstrate that RRD outperforms traditional KD techniques. Specifically, it surpasses 13 state-of-the-art methods when evaluated on the CIFAR-100 dataset. Furthermore, the method's robustness is confirmed by its successful application to other datasets such as Tiny ImageNet and STL-10, indicating its versatility and potential for broader application.

Implications

This paper suggests that focusing on relational aspects and relaxed constraints can improve the efficiency of knowledge transfer in KD. RRD could pave the way for more efficient student models that retain the performance capabilities of larger teacher models, making it highly relevant for deployment in real-world scenarios with limited computational resources.

The authors plan to release the code for RRD, which could facilitate further research and application in this area.