【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...
这样我们的损失大大提高了学习到的嵌入空间的质量。 Review of Proxy-NCA Loss 在标准设置中,Proxy-nca损失为每个类分配一个代理,以便代理的数量与类标签的数量相同。给定一个输入数据点作为锚点,这个代理和同一类的输入被认为是正的,其他的代理是负的。设x表示输入的嵌入向量,p+为正代理,p-为负代理。损失是给...
If you are also interested in new knowlege distillation method for metric learning, please check the following arxiv and repository links. The new repository has been refactored based on the Proxy-Anchor Loss implementation, so those who have used this repository will be able to use new code ...
Prototype-Based Support Example Miner and Triplet Loss for Deep Metric Learning Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of simi... S Yang,Y Zhang,Q Zhao,... - Electronics (2079-9292) 被...
Official PyTorch code for the NeurIPS 2020 spotlight paper "Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies". In this paper, we propose a novel graph-based deep metric learning loss, namelyProxyGML, which is simple to implement. The pipeline of ProxyGML is as show...
Non-isotropy Regularization for Proxy-based Deep Metric Learning 基于代理的深度度量学习中的非各向同性正则化 Abstract 深度度量学习(DML)的目的是学习可以简单地表示语义关系的表示空间。最好的方法是利用类代理作为样本替代,以更好地收敛和泛化。然而,这些代理方法只优化了样本代理的距离。由于所使用的距离函数固有...
Proxy-based Deep Metric Learning (DML) learns deep representations by embedding images close to their class representatives (proxies), commonly with respect to the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context such as class- or image-...
To sum up, the proposed method can learn more effective embeddings based on the users' interests for multiple clustering tasks. 5. Conclusion To conclude, our study thoroughly investigates the signif- icant challenges that current advanced deep learning tech- niques face...
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....
Global Proxy-based Hard Mining for Visual Place Recognition Learning deep representations for visual place recognition is commonly performed using pairwise or triple loss functions that highly depend on the hardness of the examples sampled at each training iteration. Existing techniques address this by ...