Dataset Condensation

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Most implemented papers archive 2025-07-28

30 shown of 35 papers with code (56 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Dataset Condensation with Gradient Matching
  10 Jun 2020 5 repositories listed
  As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive.
• Dataset Condensation with Distribution Matching
  8 Oct 2021 4 repositories listed
  Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets.
• Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
  29 Nov 2023 3 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 7 pointer-only (licence)
  We call this perspective "generalized matching" and propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this work, which aims to create a synthetic dataset with densities, ensuring consistency with…
• Condensing Graphs via One-Step Gradient Matching
  15 Jun 2022 3 repositories listed Syntology ran 1 of 4 samples · 3 unverified
  However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved…
• Synthesizing Informative Training Samples with GAN
  15 Apr 2022 3 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 2 pointer-only (licence)
  However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks.
• M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
  26 Dec 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Nowadays, optimization-oriented methods have been the primary method in the field of dataset condensation for achieving SOTA results.
• Improved Distribution Matching for Dataset Condensation
  19 Jul 2023 2 repositories listed Syntology ran 6 of 13 samples · 7 unverified · 13 pointer-only (licence)
  In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising.
• Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
  22 Jun 2023 2 repositories listed Syntology ran 1 of 6 samples · 5 unverified · 6 pointer-only (licence)
  The proposed method demonstrates flexibility across diverse dataset scales and exhibits multiple advantages in terms of arbitrary resolutions of synthesized images, low training cost and memory consumption with…
• Efficient Dataset Distillation Using Random Feature Approximation
  21 Oct 2022 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  Dataset distillation compresses large datasets into smaller synthetic coresets which retain performance with the aim of reducing the storage and computational burden of processing the entire dataset.
• DC-BENCH: Dataset Condensation Benchmark
  20 Jul 2022 2 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.
• Dataset Condensation via Efficient Synthetic-Data Parameterization
  30 May 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
• CAFE: Learning to Condense Dataset by Aligning Features
  3 Mar 2022 2 repositories listed Syntology ran 27 of 33 samples · 6 unverified · 33 pointer-only (licence)
  Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
• Dataset Condensation with Contrastive Signals
  7 Feb 2022 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.
• Dataset Condensation with Differentiable Siamese Augmentation
  16 Feb 2021 2 repositories listed
  In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load.
• A Large-Scale Study on Video Action Dataset Condensation
  30 Dec 2024 1 repository listed Syntology ran 3 of 17 samples · 14 unverified
  Specifically, our work delves into three key aspects to provide valuable empirical insights: (1) temporal processing of video data, (2) establishing a comprehensive evaluation protocol for video dataset condensation,…
• Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
  23 Dec 2024 1 repository listed
  The main bottleneck of the above paradigms is whether the effective information of the original graph is fully preserved when consenting to the primary sub-scale (the first of multiple scales), which determines the…
• Decomposed Distribution Matching in Dataset Condensation
  6 Dec 2024 1 repository listed
  Conventionally, DC relies on a costly bi-level optimization which prohibits its practicality.
• Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
  22 Sep 2024 1 repository listed Syntology ran 6 of 9 samples · 3 unverified · 9 pointer-only (licence)
  In response to this limitation, we introduce a novel method, Heterogeneous Model Dataset Condensation (HMDC), designed to produce universally applicable condensed images through cross-model interactions.
• Mitigating Bias in Dataset Distillation
  6 Jun 2024 1 repository listed
  Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks.
• CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
  4 Jun 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  To mitigate this gap, we theoretically analyze the optimization objective of dataset condensation for TS-forecasting and propose a new one-line plugin of dataset condensation designated as Dataset Condensation for Time…
• DANCE: Dual-View Distribution Alignment for Dataset Condensation
  3 Jun 2024 1 repository listed Syntology ran 14 of 18 samples · 4 unverified · 18 pointer-only (licence)
  Specifically, from the inner-class view, we construct multiple "middle encoders" to perform pseudo long-term distribution alignment, making the condensed set a good proxy of the real one during the whole training…
• Elucidating the Design Space of Dataset Condensation
  21 Apr 2024 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity and realism.
• Distilling Datasets Into Less Than One Image
  18 Mar 2024 1 repository listed
  Current methods frame this as maximizing the distilled classification accuracy for a budget of K distilled images-per-class, where K is a positive integer.
• Dataset Condensation for Time Series Classification via Dual Domain Matching
  12 Mar 2024 1 repository listed Syntology ran 5 of 9 samples · 4 unverified · 9 pointer-only (licence)
  Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains.
• Multisize Dataset Condensation
  10 Mar 2024 1 repository listed Syntology ran 15 of 19 samples · 4 unverified
  These two challenges connect to the "subset degradation problem" in traditional dataset condensation: a subset from a larger condensed dataset is often unrepresentative compared to directly condensing the whole dataset…
• Is Adversarial Training with Compressed Datasets Effective?
  8 Feb 2024 1 repository listed
  This synthetic dataset retains the essential information of the original dataset, enabling models trained on it to achieve performance levels comparable to those trained on the full dataset.
• Dataset Condensation Driven Machine Unlearning
  31 Jan 2024 1 repository listed
  To achieve this goal, we propose new dataset condensation techniques and an innovative unlearning scheme that strikes a balance between machine unlearning privacy, utility, and efficiency.
• You Only Condense Once: Two Rules for Pruning Condensed Datasets
  21 Oct 2023 1 repository listed Syntology ran 12 of 16 samples · 4 unverified · 16 pointer-only (licence)
  However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited…
• Fast Graph Condensation with Structure-based Neural Tangent Kernel
  17 Oct 2023 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
• Dataset Condensation for Recommendation
  2 Oct 2023 1 repository listed
  Specifically, we model the discrete user-item interactions via a probabilistic approach and design a pre-augmentation module to incorporate the potential preferences of users into the condensed datasets.

Syntology lines on 20 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.