Spectral Metric for Dataset Complexity Assessment

Branchaud-Charron, Frédéric; Achkar, Andrew; Jodoin, Pierre-Marc

doi:10.1109/cvpr.2019.00333

Cited by 22 publications

(20 citation statements)

References 38 publications

(58 reference statements)

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“…For comparison with other quality measures, we chose F1, N1, and N3 from Ho and Basu [1] and CSG from Branchaud-Charron et al [3]. Here, N1, N3, and CSG are known to be highly correlated with test accuracy of classifiers Branchaud-Charron et al [3]. F1 is similar to our M sep in its basic idea.…”

Section: Experiments Resultsmentioning

confidence: 99%

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Cho¹,

Lee²

2021

Preprint

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Section: Experiments Resultsmentioning

confidence: 99%

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Cho¹,

Lee²

2021

Preprint

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“…DA is a tradeoff between noise vs. knowledge injection, so it could be a nice theoretic direction to think about DA under statistical query model (Kearns, 1998) with translation between formal languages (ws-, 2019). This could inspire another essential question: what is the intrinsic properties of the augmented data (Branchaud-Charron et al, 2019) that matter in discrete domain. Applications like active data selection (Coleman et al, 2019) guided with margin or sensitivity can be derived.…”

Section: Discussionmentioning

confidence: 99%

Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization

Liu

Huang

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Many Data Augmentation (DA) methods have been proposed for neural machine translation. Existing works measure the superiority of DA methods in terms of their performance on a specific test set, but we find that some DA methods do not exhibit consistent improvements across translation tasks. Based on the observation, this paper makes an initial attempt to answer a fundamental question: what benefits, which are consistent across different methods and tasks, does DA in general obtain? Inspired by recent theoretic advances in deep learning, the paper understands DA from two perspectives towards the generalization ability of a model: input sensitivity and prediction margin, which are defined independent of specific test set thereby may lead to findings with relatively low variance. Extensive experiments show that relatively consistent benefits across five DA methods and four translation tasks are achieved regarding both perspectives.

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“…More recent work for characterisation of large image datasets is shown by Branchaud-Charron et al [2019].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Neighbourhoods for the Discovery of Adversarial Examples

Morgan,

Paiement,

Pauly

et al. 2021

Preprint

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Deep Neural Networks (DNNs) have often supplied state-of-the-art results in pattern recognition tasks. Despite their advances, however, the existence of adversarial examples have caught the attention of the community. Many existing works have proposed methods for searching for adversarial examples within fixed-sized regions around training points. Our work complements and improves these existing approaches by adapting the size of these regions based on the problem complexity and data sampling density. This makes such approaches more appropriate for other types of data and may further improve adversarial training methods by increasing the region sizes without creating incorrect labels.

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Spectral Metric for Dataset Complexity Assessment

Cited by 22 publications

References 38 publications

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization

Adaptive Neighbourhoods for the Discovery of Adversarial Examples

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