# Zero-Shot Coreset Selection via Iterative Subspace Sampling arXiv: https://arxiv.org/abs/2411.15349 ยท HTML: https://mlsanity.com/paper/2411.15349/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 59 references that are in the map. - First posted on arXiv 22 Nov 2024. ## Builds on (most cited first, 30 of 35) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017) - [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018) - [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019) - [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021) - [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022) - [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) - [Gradient-based Hyperparameter Optimization through Reversible Learning](https://mlsanity.com/paper/1502.03492/) (2015) - [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019) - [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://mlsanity.com/paper/1911.02590/) (2019) - [SemDeDup: Data-efficient learning at web-scale through semantic deduplication](https://mlsanity.com/paper/2303.09540/) (2023) - [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021) - [Identifying Mislabeled Data using the Area Under the Margin Ranking](https://mlsanity.com/paper/2001.10528/) (2020) - [Random Search and Reproducibility for Neural Architecture Search](https://mlsanity.com/paper/1902.07638/) (2019) - [DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning](https://mlsanity.com/paper/2204.08499/) (2022) - [Dataset Pruning: Reducing Training Data by Examining Generalization Influence](https://mlsanity.com/paper/2205.09329/) (2022) - [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021) - [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023) - [Coverage-centric Coreset Selection for High Pruning Rates](https://mlsanity.com/paper/2210.15809/) (2022) - [Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent](https://mlsanity.com/paper/1802.01737/) (2018) - [Large-scale Dataset Pruning with Dynamic Uncertainty](https://mlsanity.com/paper/2306.05175/) (2023) - [Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning](https://mlsanity.com/paper/2311.13613/) (2023) ## Built on by (most cited first, 10 of 10) - [X-Factor: Quality Is a Dataset-Intrinsic Property](https://mlsanity.com/paper/2505.22813/) (2025) - [AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training](https://mlsanity.com/paper/2507.00049/) (2025) - [Coreset Selection via LLM-based Concept Bottlenecks](https://mlsanity.com/paper/2502.16733/) (2025) - [Effective Data Pruning through Score Extrapolation](https://mlsanity.com/paper/2506.09010/) (2025) - [FT-NCFM: An Influence-Aware Data Distillation Framework for Efficient VLA Models](https://mlsanity.com/paper/2511.16233/) (2025) - [LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models](https://mlsanity.com/paper/2605.27254/) (2026) - [Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling](https://mlsanity.com/paper/2605.23198/) (2026) - [Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation](https://mlsanity.com/paper/2607.05891/) (2026) - [Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection](https://mlsanity.com/paper/2608.21754/) (2026) - [Label-Free Coreset Selection with Foundation Models for Efficient Annotation in Computational Pathology](https://mlsanity.com/paper/2610.05987/) (2026) ## Abstract (arXiv) Deep learning increasingly relies on massive data with substantial storage, annotation, and training costs. To reduce costs, coreset selection finds a representative subset of data to train models while ideally performing on par with the full data training. To maximize performance, current state-of-the-art coreset methods select data using dataset-specific ground truth labels and training. However, these methodological requirements prevent selection at scale on real-world, unlabeled data. To that end, this paper addresses the selection of coresets that achieve state-of-the-art performance but without using any labels or training on candidate data. Instead, our solution, Zero-Shot Coreset Selection via Iterative Subspace Sampling (ZCore), uses previously-trained foundation models to generate zero-shot, high-dimensional embedding spaces to interpret unlabeled data. ZCore then iteratively quantifies the relative value of all candidate data based on coverage and redundancy in numerous subspace distributions. Finally, ZCore selects a coreset sized for any data budget to train downstream models. We evaluate ZCore on four datasets and outperform several state-of-the-art label-based methods, especially at low data rates that provide the most substantial cost reduction. On ImageNet, ZCore selections for 10% training data achieve a downstream validation accuracy of 53.99%, which outperforms prior label-based methods and removes annotation and training costs for 1.15 million images. Our paper's code is publicly available at https://github.com/voxel51/zcore.