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Deep learning's reliance on massive datasets leads to significant costs in storage, annotation, and training. Although coreset selection aims to mitigate these costs by finding performant data subsets, state-of-the-art methods typically require expensive ground-truth labels and dataset-specific training. To overcome these scalability issues, ZCore introduces a zero-shot approach that functions without labels or prior training on candidate data. Instead, ZCore uses foundation models to generate a zero-shot embedding space for unlabeled data, then quantifies the relative importance of each example based on overall coverage and redundancy within the embedding distribution. On ImageNet, ZCore outperforms previous label-based methods at a 90% prune rate while eliminating the need to annotate over one million images.

We present ENCORE, a post-hoc out-of-distribution (OOD) detection method grounded in the geometric properties of neural collapse in deep neural networks. By leveraging the observation that in-distribution features align with class means while OOD features tend to be misaligned or orthogonal, ENCORE modifies inference through cosine-based scoring and adaptive feature scaling to enhance separation between known and unknown inputs. The method approximates neural collapse behavior at test time without requiring retraining, enabling more reliable uncertainty estimation. It is lightweight, memory-efficient, and compatible with a wide range of architectures, including convolutional networks and vision transformers. Extensive experiments on standard benchmarks demonstrate consistent improvements over existing OOD detection approaches in both near- and far-distribution shifts.