Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification
Abstract
The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.
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Automating data curation is necessary to cut manual input, and fewer labels should be needed for good performance.
“The data curation process must be automated to reduce manual input, and the number of labels required for good performance must be reduced [57].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [57]
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Self-supervised learning: A succinct review
Rani, V., Nabi, S. T., Kumar, M., Mittal, A., & Kumar, K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09884-2 · Cited by 295 · see citing works
- Authors
- Taherkhani, F., Dabouei, A., Soleymani, S., Dawson, J., & Nasrabadi, N. M.
- Affiliations
- West Virginia University (US)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 12262-12272
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3181871665
- Cited by
- 45
- References
- 74
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 10 · 2023: 16 · 2022: 9 · 2021: 3
- FWCI
- 4.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- semi-supervised image classification, pseudo-labeling, Wasserstein distance, self-supervised learning, unlabeled data, cluster matching, distribution discrepancy, convolutional neural networks
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