Post Training Quantization for Efficient Dataset Condensation

Abstract

Dataset Condensation (DC) distills knowledge from large datasets into smaller ones, accelerating training and reducing storage requirements. However, despite notable progress, prior methods have largely overlooked the potential of quantization for further reducing storage costs. In this paper, we take the first step to explore post-training quantization in dataset condensation, demonstrating its effectiveness in reducing storage size while maintaining representation quality without requiring expensive training cost. However, we find that at extremely low bit-widths (e.g., 2-bit), conventional quantization leads to substantial degradation in representation quality, negatively impacting the networks trained on these data. To address this, we propose a novel \emph{patch-based post-training quantization} approach that ensures localized quantization with minimal loss of information. To reduce the overhead of quantization parameters, especially for small patch sizes, we employ quantization-aware clustering to identify similar patches and subsequently aggregate them for efficient quantization. Furthermore, we introduce a refinement module to align the distribution between original images and their dequantized counterparts, compensating for quantization errors. Our method is a plug-and-play framework that can be applied to synthetic images generated by various DC methods. Extensive experiments across diverse benchmarks including CIFAR-10/100, Tiny ImageNet, and ImageNet subsets demonstrate that our method consistently outperforms prior works under the same storage constraints. Notably, our method nearly \textbf{doubles the test accuracy} of existing methods at extreme compression regimes (e.g., 26.0\% $\rightarrow$ 54.1\% for DM at IPC=1), while operating directly on 2-bit images without additional distillation.

Figures (5)

• Figure 1: Performance comparison when applying our framework to DSA pmlr-v139-zhao21a, DM zhao2022dataset, and DATM guo2024towards. Notably, our framework doubles the performance for DSA and DM at a budget of 1 Image Per Class (IPC), demonstrating its effectiveness in extremely low-storage scenarios.
• Figure 2: Overview of the proposed patch-based quantization framework for dataset condensation. (I) Synthetic images are first refined using a quantization-aware loss. (II) Patches are grouped based on quantization parameters via $k$-means clustering. (III) Each group is quantized with shared parameters, and the result is entropy encoded to produce the final compressed dataset.
• Figure 3: Visualization comparison of quantization strategies on synthetic images: (a) original images, (b) Median Cut mediancut, (c) asymmetric quantization, and (d) our patch-based quantization. All quantized images use 2-bit precision. (e) shows distortion (MSE) versus bit-width across methods. Images are generated by DM zhao2022dataset on CIFAR-10.
• Figure 4: Measured test accuracy (%) and storage across different numbers of groups. The vertical dotted line marks the maximum allowable storage under the given budget.
• Figure 5: Visualization of (a) original synthetic images and their quantized versions using (b) Median Cut, (c) Asymmetric Quantization (AQ), and (d) Group-Aware Quantization (GAQ). While Median Cut preserves texture but distorts color, and AQ preserves color but loses fine details, GAQ achieves a better balance between texture and color fidelity.