Selection baseline · Coreset selection
BPS
Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution
Guanghui Shi, Xuefeng Liang, Qixiang Wen
CVPR 2026 · first public 2026-01
In one paragraph
Models each class as multiple visual-pattern clusters via a hierarchical semantic structure, instead of assuming one cluster per class, and selects a balanced coreset of complementary class-general (center) and marginal (boundary) real samples from every cluster; proves the selected coreset aligns with the original dataset in information coverage and reports state-of-the-art results across four benchmarks with model-agnostic, one-shot-for-all-IPCs selection.
Where it sits
- Coreset selection (Selection)
- Setting: Image classification
BibTeX (generated; prefer the venue's official entry)
@article{shi2026balanced,
title = {Balanced Dataset Distillation via Modeling Multiple Visual Pattern Distribution},
author = {Guanghui Shi and Xuefeng Liang and Qixiang Wen},
journal = {CVPR 2026},
year = {2026}
}Nearby in Coreset selection
D2C — Accelerating Diffusion Model Training under Minimal Budgets: A Condensation-Based Perspective
Rui Huang, Shitong Shao, Zikai Zhou et al. · CVPR 2026notablePre-training & transferpaper ↗
OD3 — OD3: Optimization-free Dataset Distillation for Object Detection
Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao et al. · ICLR 2026notableDense predictionpaper ↗code ↗
Forgetting — An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes et al. · ICLR 2019notablepaper ↗
k-Center coreset — Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener, Silvio Savarese · ICLR 2018notablepaper ↗