Fetching the paper…
Reading the bibliography…
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images.
Signature verification using a ”siamese” time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
Earlier work this paper cites.
Neighbourhood components analysis
Jacob Goldberger, Geoffrey E Hinton, Sam T Roweis, and Ruslan R Salakhutdinov · 2005
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Large scale online learning of image similarity through ranking
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio · 2010
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Learning visual similarity for product design with convolutional neural networks
Sean Bell and Kavita Bala · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch · 2015
Earlier work this paper cites.
Metric learning with adaptive density discrimination
Oren Rippel, Manohar Paluri, Piotr Dollar, and Lubomir Bourdev · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Weldon: Weakly supervised learning of deep convolutional neural networks
Thibaut Durand, Nicolas Thome, and Matthieu Cord · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Deep clustering: Discriminative embeddings for segmentation and separation
J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe · 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.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Later among the works it cites.
Deep metric learning with angular loss
Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin · 2017
Later among the works it cites.
Sampling matters in deep embedding learning
Chao-Yuan Wu, R Manmatha, Alexander J Smola, and Philipp Krahenbuhl · 2017
Later among the works it cites.
Deep metric learning with hierarchical triplet loss
Weifeng Ge · 2018
Later among the works it cites.
Learning discriminative features with multiple granularities for person re-identification
Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, and Xi Zhou · 2018
Later among the works it cites.
Improving generalization via scalable neighborhood component analysis
Zhirong Wu, Alexei A Efros, and Stella X Yu · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Learning deep embeddings with histogram loss
Evgeniya Ustinova and Victor Lempitsky · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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.
Bier - boosting independent embeddings robustly
Michael Opitz, Georg Waltner, Horst Possegger, and Horst Bischof · 2017
Cited alongside, same era.
Deep randomized ensembles for metric learning
Hong Xuan, Richard Souvenir, and Robert Pless · 2018
Later among the works it cites.
Metric learning with horde: High-order regularizer for deep embeddings
Pierre Jacob, David Picard, Aymeric Histace, and Edouard Klein · 2019
Later among the works it cites.
Divide and conquer the embedding space for metric learning
Artsiom Sanakoyeu, Vadim Tschernezki, Uta Buchler, and Bjorn Ommer · 2019
Later among the works it cites.
Multi-similarity loss with general pair weighting for deep metric learning
Xun Wang, Xintong Han, Weilin Huang, Dengke Dong, and Matthew R Scott · 2019
Later among the works it cites.
Classification is a strong baseline for deep metric learning
Andrew Zhai, Hao-Yu Wu, and US San Francisco · 2019
Later among the works it cites.
Pyramidal person re-identification via multi-loss dynamic training
Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, and Rongrong Ji · 2019
Later among the works it cites.