Learning Deep Optimal Embeddings with Sinkhorn Divergences

14 September 2022

Mehrtash Harandi

Papers citing "Learning Deep Optimal Embeddings with Sinkhorn Divergences"

23 / 23 papers shown

Title
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling Qi Qian Lei Shang Baigui Sun Juhua Hu Hao Li Rong Jin 66 374 0 11 Sep 2019
Divide and Conquer the Embedding Space for Metric Learning A. Sanakoyeu Vadim Tschernezki U. Büchler Bjorn Ommer SSL 83 107 0 14 Jun 2019
Learning to Learn from Noisy Labeled Data Junnan Li Yongkang Wong Qi Zhao Mohan Kankanhalli NoLa 62 334 0 13 Dec 2018
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent Noah Golmant N. Vemuri Z. Yao Vladimir Feinberg A. Gholami Kai Rothauge Michael W. Mahoney Joseph E. Gonzalez 75 73 0 30 Nov 2018
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences Jean Feydy Thibault Séjourné François-Xavier Vialard S. Amari A. Trouvé Gabriel Peyré OT 64 531 0 18 Oct 2018
Deep Metric Learning with Hierarchical Triplet Loss Weifeng Ge Weilin Huang Dengke Dong Matthew R. Scott 174 413 0 16 Oct 2018
Revisiting Small Batch Training for Deep Neural Networks Dominic Masters Carlo Luschi ODL 69 668 0 20 Apr 2018
Computational Optimal Transport Gabriel Peyré Marco Cuturi OT 224 2,150 0 01 Mar 2018
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly M. Opitz Georg Waltner Horst Possegger Horst Bischof FedML OOD 88 166 0 15 Jan 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 258 1,898 0 28 Dec 2017
Sampling Matters in Deep Embedding Learning Chaoxia Wu R. Manmatha Alex Smola Philipp Krahenbuhl 114 923 0 23 Jun 2017
Learning Generative Models with Sinkhorn Divergences Aude Genevay Gabriel Peyré Marco Cuturi OT 190 631 0 01 Jun 2017
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning Akash Srivastava Lazar Valkov Chris Russell Michael U. Gutmann Charles Sutton SyDa GAN 62 678 0 22 May 2017
No Fuss Distance Metric Learning using Proxies Yair Movshovitz-Attias Alexander Toshev Thomas Leung Sergey Ioffe Saurabh Singh 89 642 0 21 Mar 2017
Learning Deep Embeddings with Histogram Loss E. Ustinova Victor Lempitsky 114 351 0 02 Nov 2016
Deep Metric Learning via Lifted Structured Feature Embedding Hyun Oh Song Yu Xiang Stefanie Jegelka Silvio Savarese FedML SSL DML 97 1,643 0 19 Nov 2015
FaceNet: A Unified Embedding for Face Recognition and Clustering Florian Schroff Dmitry Kalenichenko James Philbin 3DH 388 13,145 0 12 Mar 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 465 43,341 0 11 Feb 2015
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 485 43,694 0 17 Sep 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 455 16,923 0 20 Dec 2013
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 220 4,288 0 04 Jun 2013
Kernels for Vector-Valued Functions: a Review Mauricio A. Alvarez Lorenzo Rosasco Neil D. Lawrence GP 210 929 0 30 Jun 2011
A Kernel Method for the Two-Sample Problem Arthur Gretton Karsten Borgwardt Malte J. Rasch Bernhard Schölkopf Alex Smola 233 2,365 0 15 May 2008