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This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces.
On the origin of number and arrangement of the places of exit on the surface of pollen-grains
Pieter Tammes · 1930
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An efficient method for generating uniformly distributed points on the surface of an n-dimensional sphere
J S Hicks and R F Wheeling · 1959
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A note on a method for generating points uniformly on n-dimensional spheres
Mervin E Muller · 1959
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The need for biases in learning generalizations
Tom M Mitchell · 1980
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Multitask learning
Rich Caruana · 1997
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Distributing many points on a sphere
Edward Saff and Amo Kuijlaars · 1997
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Learning to rank using gradient descent
Christopher Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Gregory N Hullender · 2005
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S arXivado, and Jeff Dean · 2013
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Prototypical priors: From improving classification to zero-shot learning
Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, and Philip Torr · 2015
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The Tammes problem for n=14
Oleg R Musin and Alexey S Tarasov · 2015
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Regressive virtual metric learning
Michaël Perrot and Amaury Habrard · 2015
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Learning using privileged information: similarity control and knowledge transfer
Vladimir Vapnik and Rauf Izmailov · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Large-margin softmax loss for convolutional neural networks
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang · 2016
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A discriminative feature learning approach for deep face recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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Unsupervised learning by predicting noise
Piotr Bojanowski and Armand Joulin · 2017
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Semi-supervised few-shot learning with prototypical networks
Rinu Boney and Alexander Ilin · 2017
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Scale-invariant learning and convolutional networks
Soumith Chintala, Marc’Aurelio Ranzato, Arthur Szlam, Yuandong Tian, Mark Tygert, and Wojciech Zaremba · 2017
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Von Mises-Fisher mixture model-based deep learning: Application to face verification
Abul Hasnat, Julien Bohné, Jonathan Milgram, Stéphane Gentric, and Liming Chen · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q Weinberger · 2017
Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Deep nearest class mean classifiers
Samantha Guerriero, Barbara Caputo, and Thomas Mensink · 2018
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Fix your classifier: the marginal value of training the last weight layer
Elad Hoffer, Itay Hubara, and Daniel Soudry · 2018
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Learning towards minimum hyperspherical energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song · 2018
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Decoupled networks
Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M Rehg, and Le Song · 2018
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Cosface: Large margin cosine loss for deep face recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Zhifeng Li, Dihong Gong, Jingchao Zhou, and Wei Liu · 2018
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Cited alongside, same era.
https://tiny-imagenet.herokuapp.com
Fei-Fei Li, Andrej Karpathy, and Justin Johnson · 2017
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.
Deep hyperspherical learning
Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, and Le Song · 2017
Cited alongside, same era.
Cosine normalization: Using cosine similarity instead of dot product in neural networks
Chunjie Luo, Jianfeng Zhan, Lei Wang, and Qiang Yang · 2017
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No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh · 2017
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Poincaré embeddings for learning hierarchical representations
Maximillian Nickel and Douwe Kiela · 2017
Cited alongside, same era.
Robust classification with convolutional prototype learning
Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, and Cheng-Lin Liu · 2018
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Ring loss: Convex feature normalization for face recognition
Yutong Zheng, Dipan K Pal, and Marios Savvides · 2018
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Infinite mixture prototypes for few-shot learning
Kelsey R Allen, Evan Shelhamer, Hanul Shin, and Joshua B Tenenbaum · 2019
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Deep learning on small datasets without pre-training using cosine loss
Björn Barz and Joachim Denzler · 2019
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Hybrid attention-based prototypical networks for noisy few-shot relation classification
Tianyu Gao, Xu Han, Zhiyuan Liu, and Maosong Sun · 2019
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Prototype adjustment for zero shot classification
Xiao Li, Min Fang, Dazheng Feng, Haikun Li, and Jinqiao Wu · 2019
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Transferrable prototypical networks for unsupervised domain adaptation
Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, and Tao Mei · 2019
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Prototypical metric transfer learning for continuous speech keyword spotting with limited training data
Harshita Seth, Pulkit Kumar, and Muktabh Mayank Srivastava · 2019
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Poincaré glove: Hyperbolic word embeddings
Alexandru Tifrea, Gary Bécigneul, and Octavian-Eugen Ganea · 2019
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