The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions

• URL: http://arxiv.org/abs/2502.05673v1
• Date: Sat, 08 Feb 2025 19:37:33 GMT
• Title: The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions
• Authors: Ping Liu, Jiawei Du,
• Abstract summary: This survey comprehensively reviews recent advances in dataset distillation. We focus on scaling to large-scale datasets such as ImageNet-1K and ImageNet-21K. We highlight breakthrough innovations, including the SRe2L framework for efficient and effective condensation. We also explore emerging applications in video and audio processing, multi-modal learning, medical imaging, and scientific computing.
• Score: 9.622221492744496
• License:
• Abstract: Dataset distillation, which condenses large-scale datasets into compact synthetic representations, has emerged as a critical solution for training modern deep learning models efficiently. While prior surveys focus on developments before 2023, this work comprehensively reviews recent advances, emphasizing scalability to large-scale datasets such as ImageNet-1K and ImageNet-21K. We categorize progress into a few key methodologies: trajectory matching, gradient matching, distribution matching, scalable generative approaches, and decoupling optimization mechanisms. As a comprehensive examination of recent dataset distillation advances, this survey highlights breakthrough innovations: the SRe2L framework for efficient and effective condensation, soft label strategies that significantly enhance model accuracy, and lossless distillation techniques that maximize compression while maintaining performance. Beyond these methodological advancements, we address critical challenges, including robustness against adversarial and backdoor attacks, effective handling of non-IID data distributions. Additionally, we explore emerging applications in video and audio processing, multi-modal learning, medical imaging, and scientific computing, highlighting its domain versatility. By offering extensive performance comparisons and actionable research directions, this survey equips researchers and practitioners with practical insights to advance efficient and generalizable dataset distillation, paving the way for future innovations.

Related papers

• Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation [18.474302012851087]
  We propose a two-stage solution for dataset distillation. First, we compress the dataset by selecting only the most informative patches to form a coreset. Next, we leverage a generative foundation model to dynamically expand this compressed set in real-time. We demonstrate a significant improvement of over 10% compared to the state-of-the-art on several large-scale dataset distillation benchmarks.
  arXiv  Detail & Related papers  (2024-12-05T23:40:27Z)
• ATOM: Attention Mixer for Efficient Dataset Distillation [17.370852204228253]
  We propose a module to efficiently distill large datasets using a mixture of channel and spatial-wise attention. By integrating both types of attention, our ATOM module demonstrates superior performance across various computer vision datasets.
  arXiv  Detail & Related papers  (2024-05-02T15:15:01Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• 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)
• AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories [18.266786462036553]
  We propose an effective DD framework named AST, standing for Alignment with Smooth and high-quality expert Trajectories. We conduct extensive experiments on datasets of different scales, sizes, and resolutions.
  arXiv  Detail & Related papers  (2023-10-16T16:13:53Z)
• Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching [7.908244841289913]
  The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase. The state-of-the-art dataset distillation methods can not expand to all deep hashing retrieval methods. We propose an efficient condensation framework that addresses these limitations by matching the feature-embedding between synthetic set and real set.
  arXiv  Detail & Related papers  (2023-05-29T13:23:55Z)
• A Comprehensive Survey of Dataset Distillation [73.15482472726555]
  It has become challenging to handle the unlimited growth of data with limited computing power. Deep learning technology has developed unprecedentedly in the last decade. This paper provides a holistic understanding of dataset distillation from multiple aspects.
  arXiv  Detail & Related papers  (2023-01-13T15:11:38Z)
• 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)
• DC-BENCH: Dataset Condensation Benchmark [79.18718490863908]
  This work provides the first large-scale standardized benchmark on dataset condensation. It consists of a suite of evaluations to comprehensively reflect the generability and effectiveness of condensation methods. The benchmark library is open-sourced to facilitate future research and application.
  arXiv  Detail & Related papers  (2022-07-20T03:54:05Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

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.