coreset-selection

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. TPAMI, 2024.

[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.

IEEE JBHI 2026 | TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models

Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning

Code implementation for ICLR 2025 paper: ELFS: Label-Free Coreset Selection with Proxy Training Dynamics

An Efficient Dataset Condensation Plugin and Its Application to Continual Learning. NeurIPS, 2023.

DataCull is a modular, light-weight data pruning library containing many dataset pruning (coreset selection) algorithm including the official Implementation of the paper, titled, RCAP: Robust, Class-Aware, Probab ilistic Dynamic Dataset Pruning

One-shot coreset selection for medical image classification, on frozen foundation-model embeddings. MICCAI 2026.

Docs: https://erasus.readthedocs.io/en/latest/ Forget data from any foundation model without retraining. Erasus surgically removes concepts, behaviors, or training samples from LLMs, VLMs, and Diffusion models using coreset selection. 90% less compute, certified removal, multimodal support.

Zero-shot unlabeled image data selection

CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection (NeurIPS 2026, under review)

an implementation of unsupervised core-set selection

Official code for "Rethinking Dataset Quantization: Efficient Coreset Selection via Semantically-Aware Data Augmentation" (TMLR 2026)

[ACCV 2022] The official repository of ''COLLIDER: A Robust Training Framework for Backdoor Data''.