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Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses.
Signature verification using a ”siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann Lecun, Eduard Säckinger, and Roopak Shah · 1994
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Neighbourhood components analysis
Jacob Goldberger, Geoffrey E Hinton, Sam T Roweis, and Ruslan R Salakhutdinov · 2005
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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ImageNet: a large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Learning fine-grained image similarity with deep ranking
Jiang Wang, Yang Song, T. Leung, C. Rosenberg, Jingbin Wang, J. Philbin, Bo Chen, and Ying Wu · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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FaceNet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Learning to compare image patches via convolutional neural networks
Sergey Zagoruyko and Nikos Komodakis · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
Cited alongside, same era.
Beyond triplet loss: A deep quadruplet network for person re-identification
Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang · 2017
Cited alongside, same era.
Smart mining for deep metric learning
Ben Harwood, Vijay Kumar B G, Gustavo Carneiro, Ian Reid, and Tom Drummond · 2017
Attention-based ensemble for deep metric learning
Wonsik Kim, Bhavya Goyal, Kunal Chawla, Jungmin Lee, and Keunjoo Kwon · 2018
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Deep metric learning with bier: Boosting independent embeddings robustly
Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Ensemble deep manifold similarity learning using hard proxies
Nicolas Aziere and Sinisa Todorovic · 2019
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A theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning
Thanh-Toan Do, Toan Tran, Ian Reid, Vijay Kumar, Tuan Hoang, and Gustavo Carneiro · 2019
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Metric learning with horde: High-order regularizer for deep embeddings
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Cited alongside, same era.
Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
Cited alongside, same era.
No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Deep metric learning via facility location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, and Kevin Murphy · 2017
Cited alongside, same era.
Sampling matters in deep embedding learning
Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, and Philipp Krahenbuhl · 2017
Cited alongside, same era.
Joint detection and identification feature learning for person search
Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang · 2017
Cited alongside, same era.
Pierre Jacob, David Picard, Aymeric Histace, and Edouard Klein · 2019
Later among the works it cites.
Deep metric learning beyond binary supervision
Sungyeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, and Suha Kwak · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Softtriple loss: Deep metric learning without triplet sampling
Qi Qian, Lei Shang, Baigui Sun, Juhua Hu, Hao Li, and Rong Jin · 2019
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Transductive episodic-wise adaptive metric for few-shot learning
Limeng Qiao, Yemin Shi, Jia Li, Yaowei Wang, Tiejun Huang, and Yonghong Tian · 2019
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Multi-similarity loss with general pair weighting for deep metric learning
Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, and Matthew R Scott · 2019
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Ranked list loss for deep metric learning
Xinshao Wang, Yang Hua, Elyor Kodirov, Guosheng Hu, Romain Garnier, and Neil M Robertson · 2019
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Deep metric learning with tuplet margin loss
Baosheng Yu and Dacheng Tao · 2019
Later among the works it cites.