Self-supervised Pretraining Enhances Dataset Distillation for Large-scale Models

Core Concepts

Self-supervised pretraining with larger variance in batch normalization statistics enables more informative data synthesis, outperforming previous supervised dataset distillation methods, especially when using larger recovery models.

Abstract

The content discusses a new approach for dataset distillation, called Self-supervised Compression for Dataset Distillation (SC-DD), which leverages self-supervised pretraining to address the limitations of existing supervised dataset distillation methods. Key highlights: Previous supervised dataset distillation methods, such as SRe2L, face challenges in effectively utilizing larger recovery models, as the channel-wise mean and variance inside the model become flatter and less informative. The authors observe that self-supervised pretraining leads to larger variances in batch normalization (BN) statistics, which provides more informative supervision signals for data synthesis. The proposed SC-DD framework separates the learning of intermediate feature distributions from the alignment of higher-level semantic information, leading to better performance compared to previous methods. Extensive experiments on CIFAR-100, Tiny-ImageNet, and ImageNet-1K datasets demonstrate that SC-DD outperforms state-of-the-art supervised dataset distillation methods, especially when using larger recovery models. The authors highlight the importance of pretraining scheme and the positive correlation between model size and performance in the dataset distillation task.

Stats

The variance across channels in the first BN layer of a self-supervised MoCo-v3-ResNet-50 model is 28016.62, while for supervised ResNet-{18, 50, 101} models, it is 0.14798, 0.00265, and 0.00253, respectively. The variance of channel-wise mean in the first BN layer of the self-supervised MoCo-v3-ResNet-50 model is 233.19, while for supervised ResNet-{18, 50, 101} models, it is 0.79, 0.12, and 0.05, respectively.

Quotes

None

Key Insights Distilled From

Self-supervised Dataset Distillation

by Muxin Zhou,Z... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07976.pdf

Deeper Inquiries

How can the insights from self-supervised pretraining be further leveraged to improve dataset distillation beyond the proposed SC-DD framework

To further leverage the insights from self-supervised pretraining to enhance dataset distillation beyond the SC-DD framework, several strategies can be considered. One approach is to explore more advanced self-supervised learning techniques that focus on capturing richer and more diverse representations of the data. For example, incorporating methods like contrastive learning with more sophisticated augmentation strategies can help in learning more robust and informative features during pretraining. Additionally, exploring multi-task self-supervised learning objectives can enable the model to learn a broader range of features that can be beneficial for dataset distillation. Another avenue for improvement is to investigate the integration of domain-specific knowledge into the self-supervised pretraining process. By incorporating domain-specific constraints or priors into the self-supervised learning tasks, the model can learn representations that are more tailored to the characteristics of the target dataset, leading to better performance in dataset distillation. Furthermore, exploring semi-supervised or weakly supervised self-supervised learning approaches can provide additional supervision signals that can enhance the quality of the learned representations for dataset distillation tasks.

What are the potential limitations or drawbacks of the self-supervised pretraining approach for dataset distillation, and how can they be addressed

While self-supervised pretraining offers several advantages for dataset distillation, there are potential limitations and drawbacks that need to be addressed. One limitation is the challenge of capturing high-level semantic information during self-supervised learning, which may result in representations that are not directly aligned with the target dataset labels. This can lead to difficulties in synthesizing high-quality data during dataset distillation. To address this limitation, techniques such as incorporating semantic alignment objectives or leveraging additional supervision signals during pretraining can help in learning more semantically meaningful representations. Another drawback is the scalability of self-supervised pretraining to larger-scale datasets and models. As the dataset and model size increase, the computational and memory requirements of self-supervised pretraining also grow, making it challenging to apply these techniques to massive datasets like ImageNet-1K. To overcome this limitation, efficient self-supervised learning algorithms, distributed training strategies, and model parallelism techniques can be explored to scale up self-supervised pretraining for dataset distillation tasks.

Given the importance of model size highlighted in this work, how can the dataset distillation task be extended to explore the potential of even larger-scale models and datasets

To explore the potential of even larger-scale models and datasets in the dataset distillation task, several avenues can be pursued. One approach is to investigate the use of state-of-the-art large-scale pretrained models, such as transformer-based models like GPT or BERT, for dataset distillation. These models have demonstrated strong performance on various natural language processing tasks and can potentially be adapted for image dataset distillation with appropriate modifications. Additionally, exploring ensemble methods that combine multiple large-scale pretrained models for dataset distillation can help in capturing a more diverse range of features and improving the overall performance of the distilled dataset. Leveraging techniques like knowledge distillation from ensemble models can further enhance the quality of the distilled data and boost the performance of downstream tasks. Furthermore, the exploration of novel data augmentation strategies, regularization techniques, and optimization algorithms tailored for large-scale models can help in improving the efficiency and effectiveness of dataset distillation with larger models and datasets. By pushing the boundaries of model size and dataset scale, researchers can unlock new possibilities for dataset distillation and advance the state-of-the-art in this field.