One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation (2310.19444v1)

Abstract: Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillation methods are designed under the assumption that the teacher and student models belong to the same model family, particularly the hint-based approaches. By using centered kernel alignment (CKA) to compare the learned features between heterogeneous teacher and student models, we observe significant feature divergence. This divergence illustrates the ineffectiveness of previous hint-based methods in cross-architecture distillation. To tackle the challenge in distilling heterogeneous models, we propose a simple yet effective one-for-all KD framework called OFA-KD, which significantly improves the distillation performance between heterogeneous architectures. Specifically, we project intermediate features into an aligned latent space such as the logits space, where architecture-specific information is discarded. Additionally, we introduce an adaptive target enhancement scheme to prevent the student from being disturbed by irrelevant information. Extensive experiments with various architectures, including CNN, Transformer, and MLP, demonstrate the superiority of our OFA-KD framework in enabling distillation between heterogeneous architectures. Specifically, when equipped with our OFA-KD, the student models achieve notable performance improvements, with a maximum gain of 8.0% on the CIFAR-100 dataset and 0.7% on the ImageNet-1K dataset. PyTorch code and checkpoints can be found at https://github.com/Hao840/OFAKD.

• The paper introduces the OFA-KD framework that aligns logits from different model architectures into a unified space for effective knowledge distillation.
• It employs an adaptive target enhancement scheme to modulate teacher signals and mitigate architecture-specific biases during training.
• Extensive experiments on CIFAR-100 and ImageNet-1K demonstrate significant performance gains, with student models improving accuracy by up to 8.0%.

Overview of "One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation"

The paper "One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation" presents a novel framework for improving the efficacy of knowledge distillation (KD) across heterogeneous model architectures. The core innovation of this work lies in overcoming the traditionally assumed constraint of homogeneous model architectures in KD, allowing effective knowledge transfer between different architectures such as CNNs, Transformers, and MLPs. The authors introduce an approach termed as the OFA-KD (One-for-All Knowledge Distillation) framework that strategically addresses feature alignment issues between disparate architectures.

Key Contributions

1. Cross-Architecture Distillation Challenges: The paper begins by analyzing the significant feature divergence observed between different model architectures using centered kernel alignment (CKA). This analysis establishes the ineffectiveness of existing hint-based methods for cross-architecture distillation, thus motivating the need for a novel distillation strategy.
2. OFA-KD Framework: The proposed OFA-KD framework features an aligned logits space which serves as a medium for feature projection. This space removes architecture-specific information, effectively mitigating the feature divergence challenge. The student model is equipped with additional exit branches that facilitate projection to this common space.
3. Adaptive Target Enhancement Scheme: The introduction of an adaptive target enhancement mechanism is another pivotal contribution. This mechanism modulates the target information, preventing the student model from being compromised by architecture-specific biases inherent in the teacher model’s features.
4. Experimental Validation: Extensive empirical validation is conducted across varied architectures, such as CNNs, Transformers, and MLPs, using notable datasets including CIFAR-100 and ImageNet-1K. The experiments highlight significant performance improvements, with student models achieving up to an 8.0% accuracy gain on CIFAR-100 and a 0.7% uplift on ImageNet-1K datasets. These improvements illustrate the effectiveness of the OFA-KD approach over baseline KD methods.

Implications and Speculation

The theoretical and practical implications of the proposed OFA-KD framework are substantial. Theoretically, it expands the scope of our understanding of knowledge distillation by bridging the gap between diverse model architectures. Practically, it provides a feasible approach to train high-performance lightweight models which are not constrained to a homologous architecture family with the teacher models. This versatility is especially relevant in modern ML deployments where newer architectures might require integration into existing pipelines dominated by different model types.

Looking ahead, the OFA-KD framework could inspire further research into the development of more generalized distillation techniques tailored for yet to emerge model architectures. There is also potential for integrating OFA-KD with other machine learning paradigms such as federated learning where model heterogeneity is often a significant challenge.

In conclusion, the paper presented is a significant contribution to the field of knowledge distillation, providing a robust method to enhance the adaptability and performance of student models across different architectural frameworks. The proposed method not only advances the current state of KD but also paves the way for more flexible and efficient model training paradigms.