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2004 conference on computer vision and pattern recognition workshop , pages=

Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories , author= · 2004

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Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling

cs.LG · 2026-05-22 · unverdicted · novelty 6.0

SemiPrune uses a small labeled subset and semi-supervised pseudo-labeling to enable supervised dataset pruning methods, achieving state-of-the-art results on domain-specific, image-corrupted, and long-tailed datasets.

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  • Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling cs.LG · 2026-05-22 · unverdicted · none · ref 63

    SemiPrune uses a small labeled subset and semi-supervised pseudo-labeling to enable supervised dataset pruning methods, achieving state-of-the-art results on domain-specific, image-corrupted, and long-tailed datasets.