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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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7865 |
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| author | Przemysław Buczkowski Marek Kozłowski Piotr Brzeziński |
| author_facet | Przemysław Buczkowski Marek Kozłowski Piotr Brzeziński |
| author_sort | Przemysław Buczkowski |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-af74977fbcd3455b80801084f6e07eb6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-af74977fbcd3455b80801084f6e07eb62025-08-20T03:58:27ZengMDPI AGApplied Sciences2076-34172025-07-011514786510.3390/app15147865Optimizing Contrastive Learning with Semi-Online Triplet MiningPrzemysław Buczkowski0Marek Kozłowski1Piotr Brzeziński2National Information Processing Institute, 00-608 Warsaw, PolandNational Information Processing Institute, 00-608 Warsaw, PolandVive Textile Recycling, 25-663 Kielce, PolandContrastive 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.https://www.mdpi.com/2076-3417/15/14/7865machine learningcontrastive learningrepresentation learningtriplet learningtriplet mining |
| spellingShingle | Przemysław Buczkowski Marek Kozłowski Piotr Brzeziński Optimizing Contrastive Learning with Semi-Online Triplet Mining Applied Sciences machine learning contrastive learning representation learning triplet learning triplet mining |
| title | Optimizing Contrastive Learning with Semi-Online Triplet Mining |
| title_full | Optimizing Contrastive Learning with Semi-Online Triplet Mining |
| title_fullStr | Optimizing Contrastive Learning with Semi-Online Triplet Mining |
| title_full_unstemmed | Optimizing Contrastive Learning with Semi-Online Triplet Mining |
| title_short | Optimizing Contrastive Learning with Semi-Online Triplet Mining |
| title_sort | optimizing contrastive learning with semi online triplet mining |
| topic | machine learning contrastive learning representation learning triplet learning triplet mining |
| url | https://www.mdpi.com/2076-3417/15/14/7865 |
| work_keys_str_mv | AT przemysławbuczkowski optimizingcontrastivelearningwithsemionlinetripletmining AT marekkozłowski optimizingcontrastivelearningwithsemionlinetripletmining AT piotrbrzezinski optimizingcontrastivelearningwithsemionlinetripletmining |