Self-Supervised Dataset Distillation for Efficient Transfer Learning

Core Concepts

The authors propose a novel self-supervised dataset distillation framework that distills an unlabeled dataset into a small set of synthetic samples, which can be effectively used for pre-training and transfer learning on various downstream tasks.

Abstract

The authors propose a novel problem of self-supervised dataset distillation, where the goal is to distill an unlabeled dataset into a small set of synthetic samples that can be effectively used for pre-training and transfer learning. Key highlights: The authors observe that existing bilevel optimization methods for dataset distillation suffer from instability when using self-supervised learning (SSL) objectives due to the randomness introduced by data augmentations or masking. They prove that the gradient of the SSL loss with randomly sampled data augmentations or masking is a biased estimator of the true gradient. To address this issue, the authors propose to use mean squared error (MSE) as the inner and outer objective functions, which does not introduce any randomness. For the inner objective, they minimize the MSE between the model's representations of the synthetic samples and their corresponding learnable target representations. For the outer objective, they minimize the MSE between the representations of the model trained on the synthetic samples and the representations of the self-supervised target model trained on the original full dataset. The authors also simplify the inner optimization by decomposing the model into a feature extractor and a linear head, and optimizing only the linear head using kernel ridge regression while freezing the feature extractor. The authors extensively validate their proposed method, KRR-ST, on various transfer learning tasks, architecture generalization, and target data-free knowledge distillation, and show that it outperforms existing supervised dataset distillation methods.

Stats

The authors use CIFAR100, TinyImageNet, and ImageNet as source datasets, and CIFAR10, Aircraft, Stanford Cars, CUB2011, Stanford Dogs, and Flowers as target datasets. The data compression ratio for the source datasets is 2% for CIFAR100 and TinyImageNet, and around 0.08% for ImageNet.

Quotes

"We have empirically found that back-propagating through data augmentation or masking is unstable and challenging." "We prove that a gradient of the self-supervised learning (SSL) loss with randomly sampled data augmentations or masking is a biased estimator of the true gradient, explaining the instability."

Key Insights Distilled From

Self-Supervised Dataset Distillation for Transfer Learning

by Dong Bok Lee... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2310.06511.pdf

Deeper Inquiries

How can the proposed self-supervised dataset distillation framework be extended to handle multiple source datasets for transfer learning

The proposed self-supervised dataset distillation framework can be extended to handle multiple source datasets for transfer learning by modifying the optimization process to incorporate information from multiple datasets. One approach could be to concatenate the distilled datasets from each source dataset and train the model on this combined dataset. This would require adjusting the inner and outer optimization objectives to account for the different sources of data and ensure that the model can effectively learn from the diverse information provided by each dataset. Additionally, techniques such as domain adaptation or domain generalization could be employed to align the representations learned from each dataset and improve the model's performance on the target tasks.

Can the authors investigate the performance of their method on other self-supervised learning objectives beyond Barlow Twins

The authors can investigate the performance of their method on other self-supervised learning objectives beyond Barlow Twins by adapting the inner optimization objective to align with different self-supervised learning methods. For example, they could explore contrastive learning objectives like SimCLR or BYOL, or non-contrastive methods such as MoCo or Barlow Twins. By incorporating these different self-supervised learning objectives into the inner optimization process, the authors can evaluate the effectiveness of their dataset distillation framework across a broader range of self-supervised learning techniques. This analysis would provide insights into the generalizability and robustness of their method across various self-supervised learning paradigms.

What are the potential applications of the distilled dataset beyond transfer learning, such as in continual learning or neural architecture search

The distilled dataset generated through the proposed self-supervised dataset distillation framework has several potential applications beyond transfer learning. One application could be in continual learning, where the distilled dataset can serve as a compact and representative memory buffer for storing past experiences. By leveraging the distilled dataset during continual learning tasks, the model can retain knowledge from previous tasks and prevent catastrophic forgetting. Additionally, the distilled dataset could be utilized in neural architecture search (NAS) to accelerate the search process by pre-training models on the distilled dataset before evaluating them on target tasks. This approach can reduce the computational cost of NAS and enable more efficient exploration of the architecture space. Furthermore, the distilled dataset can be used for data augmentation, semi-supervised learning, or domain adaptation tasks, enhancing the model's performance and generalization capabilities across various domains and tasks.