Proxy-nca损失使快速收敛,由于其低训练复杂度,O(MC),其中M是训练数据的数目,C为类,明显低于成对训练的O(M^2)或O(M^3)。此外,代理对异常值和噪声标签具有鲁棒性,因为它们被训练来表示数据组。但是,由于损失只将每个嵌入向量与代理关联起来,因此不能利用数据到数据的关系。这一缺点限制了嵌入使用Proxy-nca损失...
【3】《Proxy Anchor Loss for Deep Metric Learning》CVPR 2020. From POSTECH, Pohang, Korea. 【4】《ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis》ArXiv 2020.04.02 From UniversityofGuelph,ON,Canada. Proxy-based Loss有加速收敛,且可以较好地缓解noise labels 和 outliers负...
Proxy Anchor Loss for Deep Metric Learning 来自 掌桥科研 喜欢 0 阅读量: 46 作者:S Kim,D Kim,M Cho,S Kwak 摘要: Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data...
Proxy Anchor Loss for Deep Metric Learning Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity....
Official PyTorch implementation of CVPR 2020 paper Proxy Anchor Loss for Deep Metric Learning. A standard embedding network trained with Proxy-Anchor Loss achieves SOTA performance and most quickly converges. This repository provides source code of experiments on four datasets (CUB-200-2011, Cars-196...
Proxy anchor loss for deep metric learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 3238–3247. [Google Scholar] Movshovitz-Attias, Y.; Toshev, A.; Leung, T.K.; Ioffe, S.; Singh, S. No fuss...
Proxy Anchor Loss for Deep Metric Learning. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020; pp. 3235–3244. [Google Scholar] [CrossRef] Movshovitz-Attias, Y.; Toshev, A.; Leung, T.K.; Ioffe, S...
Proxy Anchor Loss for Deep Metric Learning. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020; pp. 3235–3244. [Google Scholar] [CrossRef] Movshovitz-Attias, Y.; Toshev, A.; Leung, T.K.; Ioffe, S...