Dataset Distillation - 1IPC

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (11 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 Distillation by Matching Training Trajectories
  22 Mar 2022 6 repositories listed
  To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset.
• 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.
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
  20 Nov 2022 3 repositories listed
  To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory.
• Embarassingly Simple Dataset Distillation
  13 Nov 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Re-examining the foundational back-propagation through time method, we study the pronounced variance in the gradients, computational burden, and long-term dependencies.
• Dataset Distillation with Convexified Implicit Gradients
  13 Feb 2023 2 repositories listed
  We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art.
• Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
  19 Nov 2022 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  The resulting algorithm sets new SOTA on ImageNet-1K: we can scale up to 50 IPCs (Image Per Class) on ImageNet-1K on a single GPU (all previous methods can only scale to 2 IPCs on ImageNet-1K), leading to the best…
• Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
  6 Jun 2022 2 repositories listed
  We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories.
• Dataset Distillation using Neural Feature Regression
  1 Jun 2022 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the meta-dataset and the inner loop trains a model on the distilled data.
• 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.
• 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.

Syntology lines on 3 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.