Generative Dataset Distillation Based on Self-knowledge Distillation

• URL: http://arxiv.org/abs/2501.04202v1
• Date: Wed, 08 Jan 2025 00:43:31 GMT
• Title: Generative Dataset Distillation Based on Self-knowledge Distillation
• Authors: Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama,
• Abstract summary: We present a novel generative dataset distillation method that can improve the accuracy of aligning prediction logits. Our approach integrates self-knowledge distillation to achieve more precise distribution matching between the synthetic and original data. Our method outperforms existing state-of-the-art methods, resulting in superior distillation performance.
• Score: 49.20086587208214
• License:
• Abstract: Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more efficient versions. In this paper, we present a novel generative dataset distillation method that can improve the accuracy of aligning prediction logits. Our approach integrates self-knowledge distillation to achieve more precise distribution matching between the synthetic and original data, thereby capturing the overall structure and relationships within the data. To further improve the accuracy of alignment, we introduce a standardization step on the logits before performing distribution matching, ensuring consistency in the range of logits. Through extensive experiments, we demonstrate that our method outperforms existing state-of-the-art methods, resulting in superior distillation performance.

Related papers

• Dataset Distillation via Committee Voting [21.018818924580877]
  We introduce $bf C$ommittee $bf V$oting for $bf D$ataset $bf D$istillation (CV-DD) CV-DD is a novel approach that leverages the collective wisdom of multiple models or experts to create high-quality distilled datasets.
  arXiv  Detail & Related papers  (2025-01-13T18:59:48Z)
• Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification [0.0]
  Main goal is to push further the performance of prototype-based soft-labels distillation in terms of classification accuracy. Experimental studies trace the capability of the method to distill the data, but also the opportunity to act as an augmentation method.
  arXiv  Detail & Related papers  (2024-03-25T19:15:19Z)
• Towards Adversarially Robust Dataset Distillation by Curvature Regularization [11.02948004359488]
  We study how to embed adversarial robustness in distilled datasets, so that models trained on these datasets maintain the high accuracy and acquire better adversarial robustness. We propose a new method that achieves this goal by incorporating curvature regularization into the distillation process with much less computational overhead than standard adversarial training.
  arXiv  Detail & Related papers  (2024-03-15T06:31:03Z)
• 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)
• Dataset Distillation via the Wasserstein Metric [35.32856617593164]
  We introduce the Wasserstein distance, a metric grounded in optimal transport theory, to enhance distribution matching in dataset distillation. Our method achieves new state-of-the-art performance across a range of high-resolution datasets.
  arXiv  Detail & Related papers  (2023-11-30T13:15:28Z)
• 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)
• Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation [96.92250565207017]
  We study the data efficiency and selection for the dataset distillation task. By re-formulating the dynamics of distillation, we provide insight into the inherent redundancy in the real dataset. We find the most contributing samples based on their causal effects on the distillation.
  arXiv  Detail & Related papers  (2023-05-28T06:53:41Z)
• 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)
• Explicit and Implicit Knowledge Distillation via Unlabeled Data [5.702176304876537]
  We propose an efficient unlabeled sample selection method to replace high computational generators. We also propose a class-dropping mechanism to suppress the label noise caused by the data domain shifts. Experimental results show that our method can quickly converge and obtain higher accuracy than other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-17T09:10:41Z)
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation [151.70234052015948]
  We propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7%.
  arXiv  Detail & Related papers  (2022-11-20T15:49:11Z)

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.