Knowledge Distillation from A Stronger Teacher (2205.10536v3)

Abstract: Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better from a stronger teacher. We empirically find that the discrepancy of predictions between the student and a stronger teacher may tend to be fairly severer. As a result, the exact match of predictions in KL divergence would disturb the training and make existing methods perform poorly. In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly. Besides, considering that different instances have different semantic similarities to each class, we also extend this relational match to the intra-class level. Our method is simple yet practical, and extensive experiments demonstrate that it adapts well to various architectures, model sizes and training strategies, and can achieve state-of-the-art performance consistently on image classification, object detection, and semantic segmentation tasks. Code is available at: https://github.com/hunto/DIST_KD .

Analysis of "Knowledge Distillation from A Stronger Teacher"

The paper "Knowledge Distillation from A Stronger Teacher" presents an innovative approach to improving the efficacy of knowledge distillation (KD) when a stronger teacher model is employed. The authors introduce a method called DIST, which addresses the constraints and challenges associated with transferring knowledge from a significantly more capable teacher model to a student model. Through empirical evaluation, they observe that existing KD techniques experience performance degradation when the teacher model's strength is markedly increased, either in network capacity or through advanced training strategies.

Theoretical and Methodological Contributions

The primary contribution of this paper is the conceptual and practical delineation of using relational considerations in the KD paradigm. Instead of relying solely on the conventional KL divergence to align the probabilistic predictions of teacher and student models, the authors propose leveraging the relations inherent in the prediction outputs. The relation-based approach focuses on preserving the inter-class and intra-class correlations between the predictions of the teacher and the student.

• Inter-Class Relation: This involves encapsulating the rank-order or preference structure of predictions across different classes, facilitating a relaxed matching that circumvents the limitations of exact prediction matching.
• Intra-Class Relation: The intra-class relation focuses on capturing the distributional spread of predictions across instances within each class, offering another dimension of insightful alignment from teacher to student.

The proposed correlation-based loss, employing Pearson correlation coefficients, serves to capture these relational aspects and promote a better alignment conducive to effective distillation.

Empirical Evaluation and Results

The authors conducted rigorous experimental validations on a suite of standard benchmark tasks, including image classification, object detection, and semantic segmentation. Here are a few noteworthy outcomes:

• Image Classification: The DIST method consistently outperformed traditional KD methods, particularly when challenging strong teachers and advanced training strategies were employed. For instance, on the ImageNet dataset, DIST achieved a remarkable 72.07% accuracy on ResNet-18 using a stronger teacher.
• Object Detection and Semantic Segmentation: The proposed method also showed substantial improvements over existing KD methods tailored for these complex tasks. The generality of the DIST approach was particularly highlighted as it managed to perform well across various architectures and model sizes.

Implications and Future Directions

The research illuminates several intriguing implications for the future of KD. The use of relational metrics such as Pearson correlation, beyond KL divergence, opens avenues for new techniques that gracefully handle stronger teacher models. Practically, this enables deploying smaller, efficient models that can effectively harness the capabilities of larger models trained with complex strategies. This becomes increasingly relevant in edge computing and resource-constrained environments.

On a theoretical plane, the work challenges the traditional semantics of model logits by suggesting that the relational information encapsulated within predictions might be more informational and generalizable. Future research could extend this analysis, exploring alternative relational metrics or hybrid approaches that balance exact predictions with relational coherence.

Conclusion

The research presented in the paper "Knowledge Distillation from A Stronger Teacher" underscores the nuanced dynamics of distillation when engaging with formidable teacher models. By shifting the focus from strict prediction matching to relational understanding, the authors provide a formidable contribution that promises enhanced performance and adaptability. This paper's insights are both pragmatic and foundational, offering a refreshed lens through which the challenges and opportunities of KD are viewed and tackled. The generalize-ability and simplicity of the DIST method make it an appealing choice for a broad range of applications, paving the pathway for further innovations in efficient model training and deployment.