Optimizing Contrastive Learning with Semi-Online Triplet Mining
Contrastive learning is a machine learning technique in which models learn by contrasting similar and dissimilar data points. Its goal is to learn a representation of data in such a way that similar instances are close together in the representation space, while dissimilar instances are far apart. O...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7865 |
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| Summary: | Contrastive learning is a machine learning technique in which models learn by contrasting similar and dissimilar data points. Its goal is to learn a representation of data in such a way that similar instances are close together in the representation space, while dissimilar instances are far apart. Our industrial use case focuses on a special case of contrastive learning called triplet learning. Building triplets with adequate difficulty is crucial to effective training convergence in such a setup. By combining online and offline mining techniques, we propose a method of mining hard triplets that is both performant and memory-inexpensive. Our experiments demonstrate that the method leads to improved identity pairing (which is the specific case of clustering) both on a real-life industry shoe dataset and on a generated benchmark one. |
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| ISSN: | 2076-3417 |