Knowledge Distillation Meets Self-Supervision (2006.07114v2)

Abstract: Knowledge distillation, which involves extracting the "dark knowledge" from a teacher network to guide the learning of a student network, has emerged as an important technique for model compression and transfer learning. Unlike previous works that exploit architecture-specific cues such as activation and attention for distillation, here we wish to explore a more general and model-agnostic approach for extracting "richer dark knowledge" from the pre-trained teacher model. We show that the seemingly different self-supervision task can serve as a simple yet powerful solution. For example, when performing contrastive learning between transformed entities, the noisy predictions of the teacher network reflect its intrinsic composition of semantic and pose information. By exploiting the similarity between those self-supervision signals as an auxiliary task, one can effectively transfer the hidden information from the teacher to the student. In this paper, we discuss practical ways to exploit those noisy self-supervision signals with selective transfer for distillation. We further show that self-supervision signals improve conventional distillation with substantial gains under few-shot and noisy-label scenarios. Given the richer knowledge mined from self-supervision, our knowledge distillation approach achieves state-of-the-art performance on standard benchmarks, i.e., CIFAR100 and ImageNet, under both similar-architecture and cross-architecture settings. The advantage is even more pronounced under the cross-architecture setting, where our method outperforms the state of the art CRD by an average of 2.3% in accuracy rate on CIFAR100 across six different teacher-student pairs.

An Examination of "Knowledge Distillation Meets Self-Supervision"

In the paper titled "Knowledge Distillation Meets Self-Supervision," the authors present an innovative approach to knowledge distillation (KD) by incorporating self-supervision tasks to enhance model performance. The primary objective is to distill "richer dark knowledge" from a teacher network to a student network, thereby improving the efficacy of model compression and transfer learning. This enriched knowledge is extracted through contrastive learning-based self-supervision tasks, which serve as an auxiliary mechanism during the knowledge distillation process.

The paper encapsulates the core of its proposal through a detailed explanation of self-supervised knowledge distillation (SSKD). The authors explore the hypothesis that integrating self-supervised learning tasks into knowledge distillation processes can provide more comprehensive insights into the internal knowledge of neural networks. Unlike traditional methods that depend heavily on architecture-specific intermediate features, SSKD leverages the teacher network's intrinsic understanding of data relationships through self-supervision signals.

Methodological Insights

The proposed SSKD method is characterized by a unique training framework involving both conventional KD and self-supervision derived from contrastive learning tasks. The contrastive learning mechanism allows for the extraction and transfer of rich, structured knowledge by encouraging agreement between transformed data representations. This methodology not only aids in understanding data transformations but also regularizes the student network, enhancing its generalization capability.

A two-stage training process is utilized for the teacher network in SSKD. The first stage focuses on classification tasks with the original architecture, while the second stage introduces self-supervision through additional transformations. During the student's training phase, mimicking occurs both on the classification outputs and on the outputs derived from the self-supervision module. The selective transfer strategy manages the influence of potentially noisy predictions, thus refining the knowledge distillation process.

Empirical Evaluation and Results

Rigorous experiments conducted on benchmarks such as CIFAR100 and ImageNet validate the proposed SSKD framework. The empirical evidence provided demonstrates that SSKD outperforms several state-of-the-art KD methods, particularly in the context of cross-architectural teacher-student pairs. Notably, SSKD achieves an average of 2.3% accuracy improvement on the CIFAR100 dataset over the competitive method CRD across multiple pairs of teacher and student architectures. The advantage of SSKD becomes even more pronounced in environments with few-shot scenarios and noisy labels.

Furthermore, the paper highlights the robustness of SSKD through linear evaluation of learned representations on auxiliary datasets, showing enhanced feature learning capabilities of the student models trained with the SSKD approach.

Broader Implications and Future Directions

The work establishes a promising link between self-supervised learning and knowledge distillation, opening up new pathways for research in transfer learning and model optimization. The concept of enhancing KD with self-supervision is particularly appealing in scenarios where model interpretability and effective resource utilization are crucial.

Looking forward, SSKD sets a precedent for exploring other self-supervision tasks and their impact on distillation performance. This work invites further investigation into developing more sophisticated models that incorporate diverse self-supervised tasks, thereby potentially broadening the application spectrum of KD to modalities beyond traditional image classification—such as natural language processing and other domains.

In conclusion, "Knowledge Distillation Meets Self-Supervision" constitutes a significant contribution towards more generalized and robust frameworks for knowledge transfer in neural networks, bolstering theoretical understanding and offering practical solutions to core challenges in contemporary AI.