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To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes.
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M. Guillaumin, J. Verbeek, and C. Schmid · 2010
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Rectified linear units improve restricted boltzmann machines
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Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
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Deep metric learning using triplet network
E. Hoffer and N. Ailon · 2014
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Descriptor learning for omnidirectional image matching
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Learning local feature descriptors using convex optimisation
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J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu · 2014
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer · 2015
Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions
B. Kumar, G. Carneiro, and I. Reid · 2016
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Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
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Improved deep metric learning with multi-class n-pair loss objective
K. Sohn · 2016
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Deep metric learning via lifted structured feature embedding
H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
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Learning deep embeddings with histogram loss
E. Ustinova and V. Lempitsky · 2016
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Learning descriptors for object recognition and 3d pose estimation
P. Wohlhart and V. Lepetit · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Fanng: Fast approximate nearest neighbour graphs
B. Harwood and T. Drummond · 2016
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Y. Yuan, K. Yang, and C. Zhang · 2016
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Fast training of triplet-based deep binary embedding networks
B. Zhuang, G. Lin, C. Shen, and I. Reid · 2016
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In defense of the triplet loss for person re-identification
A. Hermans, L. Beyer, and B. Leibe · 2017
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No fuss distance metric learning using proxies
Y. Movshovitz-Attias, A. Toshev, T. K. Leung, S. Ioffe, and S. Singh · 2017
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Deep metric learning via facility location
H. Oh Song, S. Jegelka, V. Rathod, and K. Murphy · 2017
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