{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:56:54Z","timestamp":1760234214388,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Person re-Identification(Re-ID) based on deep convolutional neural networks (CNNs) achieves remarkable success with its fast speed. However, prevailing Re-ID models are usually built upon backbones that manually design for classification. In order to automatically design an effective Re-ID architecture, we propose a pedestrian re-identification algorithm based on knowledge distillation, called KDAS-ReID. When the knowledge of the teacher model is transferred to the student model, the importance of knowledge in the teacher model will gradually decrease with the improvement of the performance of the student model. Therefore, instead of applying the distillation loss function directly, we consider using dynamic temperatures during the search stage and training stage. Specifically, we start searching and training at a high temperature and gradually reduce the temperature to 1 so that the student model can better learn from the teacher model through soft targets. Extensive experiments demonstrate that KDAS-ReID performs not only better than other state-of-the-art Re-ID models on three benchmarks, but also better than the teacher model based on the ResNet-50 backbone.<\/jats:p>","DOI":"10.3390\/a14050137","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T02:31:43Z","timestamp":1619490703000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhou","family":"Lei","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China"}]},{"given":"Kangkang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China"}]},{"given":"Kai","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China"}]},{"given":"Shengbo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. 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