Patel, D and Sastry, PS (2023) Adaptive Sample Selection for Robust Learning under Label Noise. In: 23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023, 3 - 7 January 2023, Waikoloa, pp. 3921-3931.
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Abstract
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms rely on sample selection strategies wherein, essentially, a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds, and it is difficult to fix or learn these thresholds. Often, these algorithms also require information such as label noise rates which are typically unavailable in practice. In this paper, we propose an adaptive sample selection strategy that relies only on batch statistics of a given mini-batch to provide robustness against label noise. The algorithm does not have any additional hyperparameters for sample selection, does not need any information on noise rates and does not need access to separate data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.1 © 2023 IEEE.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Learning algorithms, Algorithm: machine learning architecture; And algorithm (including transfer); Formulation; Labeled data; Learning architectures; Machine-learning; Noise rate; Overfitting; Robust learning; Samples selection, Deep neural networks |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 15 Mar 2023 05:50 |
| Last Modified: | 15 Mar 2023 05:50 |
| URI: | https://eprints.iisc.ac.in/id/eprint/80985 |
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