# Dataset Condensation with Color Compensation arXiv: https://arxiv.org/abs/2508.01139 ยท HTML: https://mlsanity.com/paper/2508.01139/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. - Builds on 40 of its 58 references that are in the map. - First posted on arXiv 2 Aug 2025. ## Placed in - Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/) ## Builds on (most cited first, 30 of 40) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016) - [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020) - [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022) - [Dataset Condensation with Distribution Matching](https://mlsanity.com/paper/2110.04181/) (2021) - [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021) - [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020) - [Dataset Distillation with Infinitely Wide Convolutional Networks](https://mlsanity.com/paper/2107.13034/) (2021) - [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory](https://mlsanity.com/paper/2211.10586/) (2022) - [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022) - [Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation](https://mlsanity.com/paper/2211.11004/) (2022) - [Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective](https://mlsanity.com/paper/2306.13092/) (2023) - [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023) - [Generalizing Dataset Distillation via Deep Generative Prior](https://mlsanity.com/paper/2305.01649/) (2023) - [Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching](https://mlsanity.com/paper/2310.05773/) (2023) - [Dataset Distillation via Factorization](https://mlsanity.com/paper/2210.16774/) (2022) - [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023) - [On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm](https://mlsanity.com/paper/2312.03526/) (2023) - [Submodular Combinatorial Information Measures with Applications in Machine Learning](https://mlsanity.com/paper/2006.15412/) (2020) - [DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2304.06648/) (2023) - [DataDAM: Efficient Dataset Distillation with Attention Matching](https://mlsanity.com/paper/2310.00093/) (2023) - [Efficient Dataset Distillation via Minimax Diffusion](https://mlsanity.com/paper/2311.15529/) (2023) - [Synthesizing Informative Training Samples with GAN](https://mlsanity.com/paper/2204.07513/) (2022) - [D4M: Dataset Distillation via Disentangled Diffusion Model](https://mlsanity.com/paper/2407.15138/) (2024) - [Dataset Quantization](https://mlsanity.com/paper/2308.10524/) (2023) ## Built on by (most cited first, 5 of 5) - [FD2: A Dedicated Framework for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2603.25144/) (2026) - [Geometry-Aware Dataset Condensation for Diffusion Model Training](https://mlsanity.com/paper/2606.05883/) (2026) - [Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks](https://mlsanity.com/paper/2601.10090/) (2026) - [IMS3: Breaking Distributional Aggregation in Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2603.13960/) (2026) - [DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2608.25480/) (2026) ## Abstract (arXiv) Dataset condensation always faces a constitutive trade-off: balancing performance and fidelity under extreme compression. Existing methods struggle with two bottlenecks: image-level selection methods (Coreset Selection, Dataset Quantization) suffer from inefficiency condensation, while pixel-level optimization (Dataset Distillation) introduces semantic distortion due to over-parameterization. With empirical observations, we find that a critical problem in dataset condensation is the oversight of color's dual role as an information carrier and a basic semantic representation unit. We argue that improving the colorfulness of condensed images is beneficial for representation learning. Motivated by this, we propose DC3: a Dataset Condensation framework with Color Compensation. After a calibrated selection strategy, DC3 utilizes the latent diffusion model to enhance the color diversity of an image rather than creating a brand-new one. Extensive experiments demonstrate the superior performance and generalization of DC3 that outperforms SOTA methods across multiple benchmarks. To the best of our knowledge, besides focusing on downstream tasks, DC3 is the first research to fine-tune pre-trained diffusion models with condensed datasets. The Frechet Inception Distance (FID) and Inception Score (IS) results prove that training networks with our high-quality datasets is feasible without model collapse or other degradation issues. Code and generated data are available at https://github.com/528why/Dataset-Condensation-with-Color-Compensation.