Self-supervised Dataset Distillation: A Good Compression Is All You Need

• URL: http://arxiv.org/abs/2404.07976v1
• Date: Thu, 11 Apr 2024 17:56:40 GMT
• Title: Self-supervised Dataset Distillation: A Good Compression Is All You Need
• Authors: Muxin Zhou, Zeyuan Yin, Shitong Shao, Zhiqiang Shen,
• Abstract summary: We introduce SC-DD, a simple yet effective Self-supervised Compression framework for dataset distillation. The proposed SC-DD outperforms all previous state-of-the-art supervised dataset distillation methods when employing larger models. Experiments are conducted on CIFAR-100, Tiny-ImageNet and ImageNet-1K datasets to demonstrate the superiority of our proposed approach.
• Score: 23.02066055996762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dataset distillation aims to compress information from a large-scale original dataset to a new compact dataset while striving to preserve the utmost degree of the original data informational essence. Previous studies have predominantly concentrated on aligning the intermediate statistics between the original and distilled data, such as weight trajectory, features, gradient, BatchNorm, etc. In this work, we consider addressing this task through the new lens of model informativeness in the compression stage on the original dataset pretraining. We observe that with the prior state-of-the-art SRe$^2$L, as model sizes increase, it becomes increasingly challenging for supervised pretrained models to recover learned information during data synthesis, as the channel-wise mean and variance inside the model are flatting and less informative. We further notice that larger variances in BN statistics from self-supervised models enable larger loss signals to update the recovered data by gradients, enjoying more informativeness during synthesis. Building on this observation, we introduce SC-DD, a simple yet effective Self-supervised Compression framework for Dataset Distillation that facilitates diverse information compression and recovery compared to traditional supervised learning schemes, further reaps the potential of large pretrained models with enhanced capabilities. Extensive experiments are conducted on CIFAR-100, Tiny-ImageNet and ImageNet-1K datasets to demonstrate the superiority of our proposed approach. The proposed SC-DD outperforms all previous state-of-the-art supervised dataset distillation methods when employing larger models, such as SRe$^2$L, MTT, TESLA, DC, CAFE, etc., by large margins under the same recovery and post-training budgets. Code is available at https://github.com/VILA-Lab/SRe2L/tree/main/SCDD/.

Related papers

• CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting [22.473436770730657]
  The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. In classification, the synthetic data is considered well-distilled if the model trained with the full dataset and the model trained with the synthetic dataset yield identical labels for the same input. In TS-forecasting, the effectiveness of synthetic data distillation is determined by the distance between predictions of the two models.
  arXiv  Detail & Related papers  (2024-06-04T09:18:20Z)
• Importance-Aware Adaptive Dataset Distillation [53.79746115426363]
  Development of deep learning models is enabled by the availability of large-scale datasets. dataset distillation aims to synthesize a compact dataset that retains the essential information from the large original dataset. We propose an importance-aware adaptive dataset distillation (IADD) method that can improve distillation performance.
  arXiv  Detail & Related papers  (2024-01-29T03:29:39Z)
• Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality [78.6359306550245]
  We argue that using just one synthetic subset for distillation will not yield optimal generalization performance. PDD synthesizes multiple small sets of synthetic images, each conditioned on the previous sets, and trains the model on the cumulative union of these subsets. Our experiments show that PDD can effectively improve the performance of existing dataset distillation methods by up to 4.3%.
  arXiv  Detail & Related papers  (2023-10-10T20:04:44Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• Generalizing Dataset Distillation via Deep Generative Prior [75.9031209877651]
  We propose to distill an entire dataset's knowledge into a few synthetic images. The idea is to synthesize a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. We present a new optimization algorithm that distills a large number of images into a few intermediate feature vectors in the generative model's latent space.
  arXiv  Detail & Related papers  (2023-05-02T17:59:31Z)
• Dataset Distillation: A Comprehensive Review [76.26276286545284]
  dataset distillation (DD) aims to derive a much smaller dataset containing synthetic samples, based on which the trained models yield performance comparable with those trained on the original dataset. This paper gives a comprehensive review and summary of recent advances in DD and its application.
  arXiv  Detail & Related papers  (2023-01-17T17:03:28Z)
• Dataset Distillation using Neural Feature Regression [32.53291298089172]
  We develop an algorithm for dataset distillation using neural Feature Regression with Pooling (FRePo) FRePo achieves state-of-the-art performance with an order of magnitude less memory requirement and two orders of magnitude faster training than previous methods. We show that high-quality distilled data can greatly improve various downstream applications, such as continual learning and membership inference defense.
  arXiv  Detail & Related papers  (2022-06-01T19:02:06Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.