ラベル不要の多様性：知覚と行動を横断するタスク非依存コアセット選択
Diversity Without Labels: Task-Agnostic Coreset Selection Across Perception and Action
視覚基盤モデルのニューロン活性化分布を画像ごと・データセット全体で比較し、ラベルやタスク知識なしに高品質なデータ部分集合（コアセット）を選ぶ手法CoreDNAを提案。分類・セグメンテーション・深度推定・運転やマニピュレーションの模倣学習で既存手法を上回る。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Julia Hindel, Benjamin Ramtoula, Kyuhwan Yeon, Nick Heppert, Paul Newman, Daniele De Martini, Abhinav Valada
分類: cs.CV
原文アブストラクト
While data collection for model training is relatively easy, curating high-quality datasets from large collections is inherently challenging. Coreset estimation methods address this problem by identifying the best data subsets to train models on. However, existing approaches often require proxy model training, extensive annotations, or task-specific tailoring. Unsupervised alternatives typically exploit geometric properties in foundation model feature spaces, but are limited to pairwise image-to-image comparisons or comparisons against a fixed subset. We introduce CoreDNA, an unsupervised, task-agnostic coreset estimation approach that pools neuron activations from vision foundation models into per-image and per-dataset distributions. By comparing each image's distribution to the dataset-level distribution, CoreDNA can capture both low-level visual structure and high-level semantic information across layers. We find that high-quality samples concentrate in the tails of the last layer's activation statistics, which we validate by relating them to training-based coreset methods. Building on these findings, we develop a computationally efficient selection mechanism to construct task-agnostic, high-quality coresets for selective annotation and model training. Across image classification, semantic segmentation, depth estimation, and imitation learning for end-to-end driving and mobile manipulation, CoreDNA matches or outperforms the state of the art, demonstrating unprecedented generalization across perception and action tasks. These results highlight the effectiveness of compact activation-based representations for data selection.