Distillation and Self-Supervised Learning: A Comprehensive Survey

In this article, we delve into the realm of self-supervised learning (SSL) and its potential to revolutionize deep learning. SSL enables models to learn from vast amounts of unlabeled data without relying on expensive annotation processes. To further enhance SSL, researchers have introduced knowledge distillation (KD), which involves transferring knowledge from a large teacher model to a smaller student model. However, existing KD methods are limited to task-specific tasks and cannot be applied to SSL directly.
To address this challenge, we propose a novel framework called DMT (Distillation from Multiple Teachers). By leveraging multiple teachers with diverse expertise, DMT can learn rich and task-agnostic representations. These representations can then be distilled into a smaller student model, enhancing its performance and compressing the model size simultaneously.
Our proposed framework consists of three stages: pre-training, fine-tuning, and distillation. In the pre-training stage, multiple teachers are employed to generate token embeddings that capture different aspects of the input data. These token embeddings are then fed into a transformer encoder for feature extraction.
In the fine-tuning stage, we adapt a small student model to learn from the extracted features and perform the target task. Finally, in the distillation stage, KD is applied to transfer the knowledge from the large teacher model to the small student model, resulting in improved performance and model compression.
We evaluate our proposed framework on several benchmark datasets, including ImageNet and CIFAR-10. The results demonstrate that DMT outperforms existing SSL methods and achieves state-of-the-art performance in various tasks. Additionally, we show that DMT can be applied to different tasks without requiring task-specific modifications, making it a versatile and generalizable approach for SSL.
In summary, our work introduces DMT, a novel framework that leverages multiple teachers to improve self-supervised learning. By transferring knowledge from diverse experts, DMT can learn rich representations, enhance model performance, and compress the model size, making it an attractive solution for various deep learning tasks.