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Are all training examples equally valuable?

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arxiv 1311.6510 v1 pith:ZRO73NA4 submitted 2013-11-25 cs.CV cs.LGstat.ML

Agata Lapedriza , Hamed Pirsiavash , Zoya Bylinskii , Antonio Torralba This is my paper · ORCID
classification cs.CVcs.LGstat.ML
keywords trainingexamplesclassifierssomebetterdetectorsequallyothers
verification ladder T0 review T1 audit T2 compute T3 formal

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When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others. The goal of this paper is to bring to the attention of the vision community the following considerations: (1) some examples are better than others for training detectors or classifiers, and (2) in the presence of better examples, some examples may negatively impact performance and removing them may be beneficial. In this paper, we propose an approach for measuring the training value of an example, and use it for ranking and greedily sorting examples. We test our methods on different vision tasks, models, datasets and classifiers. Our experiments show that the performance of current state-of-the-art detectors and classifiers can be improved when training on a subset, rather than the whole training set.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dataset Distillation

    cs.LG 2018-11 unverdicted novelty 8.0 of 10

    Dataset distillation creates a tiny synthetic training set that, when used with a fixed network initialization, produces models whose performance approximates that of models trained on the full original dataset.

  2. Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development

    cs.LG 2026-07 conditional novelty 5.0 of 10

    In a stylized model, a proactive flywheel that fixes whole groups of related scenarios needs Θ(K log K) update rounds versus Θ(M log M) for reactive patching.

  3. Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification

    cs.CV 2025-07 conditional novelty 4.0 of 10

    IMET, a hybrid of equal-class and misclassification-weighted sampling, reports 80.3% and 90.2% accuracy on OCTMNIST and PneumoniaMNIST with a 30k-parameter CNN.

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