Dynamic sampling for deep metric learning
| Autor: | Chang-Hui Liang, Wan-Lei Zhao, Run-Qing Chen |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Process (computing) Sampling (statistics) Pattern recognition Sample (statistics) Manifold Image (mathematics) Artificial Intelligence Simple (abstract algebra) Signal Processing Metric (mathematics) Embedding Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | Pattern Recognition Letters. 150:49-56 |
| ISSN: | 0167-8655 |
| Popis: | Deep metric learning maps visually similar images onto nearby locations and visually dissimilar images apart from each other in an embedding manifold. The learning process is mainly based on the supplied image negative and positive training pairs. In this paper, a dynamic sampling strategy is proposed to organize the training pairs in an easy-to-hard order to feed into the network. It allows the network to learn general boundaries between categories from the easy training pairs at its early stages and finalize the details of the model mainly relying on the hard training samples in the later. Compared to the existing training sample mining approaches, the hard samples are mined with little harm to the learned general model. This dynamic sampling strategy is formularized as two simple terms that are compatible with various loss functions. Consistent performance boost is observed when it is integrated with several popular loss functions on fashion search, fine-grained classification, and person re-identification tasks. Comment: 11 pages, 4 figures |
| Databáze: | OpenAIRE |
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