Fetching the paper…
Reading the bibliography…
Labeling a classification dataset implies to define classes and associated coarse labels, that may approximate a smoother and more complicated ground truth.
Comparing biases for minimal network construction with back-propagation
Stephen Hanson and Lorien Pratt · 1988
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
Earlier work this paper cites.
One-shot learning of object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
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.
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Agenet: Deeply learned regressor and classifier for robust apparent age estimation
Xin Liu, Shaoxin Li, Meina Kan, Jie Zhang, Shuzhe Wu, Wenxian Liu, Hu Han, Shiguang Shan, and Xilin Chen · 2015
Earlier work this paper cites.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Earlier work this paper cites.
Dex: Deep expectation of apparent age from a single image
Rasmus Rothe, Radu Timofte, and Luc Van Gool · 2015
Earlier work this paper cites.
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.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Regularizing prediction entropy enhances deep learning with limited data
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Iyad Rahwan, and Nikhil Naik · 2017
Cited alongside, same era.
Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, and Antonio Plaza · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Maximum-entropy fine grained classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
Later among the works it cites.
Deep expectation of real and apparent age from a single image without facial landmarks
Rasmus Rothe, Radu Timofte, and Luc Van Gool · 2018
Later among the works it cites.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Later among the works it cites.
Revisit knowledge distillation: a teacher-free framework
Li Yuan, Francis EH Tay, Guilin Li, Tao Wang, and Jiashi Feng · 2019
Later among the works it cites.
Meta learning with differentiable closed-form solver for fast video object segmentation
Yu Liu, Lingqiao Liu, Haokui Zhang, Hamid Rezatofighi, Qingsen Yan, and Ian Reid · 2020
Later among the works it cites.
Generalized entropy regularization or: There’s nothing special about label smoothing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
Cited alongside, same era.
On loss functions for deep neural networks in classification
Katarzyna Janocha and Wojciech Marian Czarnecki · 2017
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
Cited alongside, same era.
Regularizing neural networks by penalizing confident output distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Łukasz Kaiser, and Geoffrey Hinton · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Cited alongside, same era.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Cited alongside, same era.
Clara Meister, Elizabeth Salesky, and Ryan Cotterell · 2020
Later among the works it cites.
Self-distillation as instance-specific label smoothing
Zhilu Zhang and Mert Sabuncu · 2020
Later among the works it cites.
Graph-based interpolation of feature vectors for accurate few-shot classification
Yuqing Hu, Vincent Gripon, and Stéphane Pateux · 2021
Later among the works it cites.
Why do better loss functions lead to less transferable features?
Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi · 2021
Later among the works it cites.
Is label smoothing truly incompatible with knowledge distillation: An empirical study
Zhiqiang Shen, Zechun Liu, Dejia Xu, Zitian Chen, Kwang-Ting Cheng, and Marios Savvides · 2021
Later among the works it cites.
Dp-ssl: Towards robust semi-supervised learning with a few labeled samples
Yi Xu, Jiandong Ding, Lu Zhang, and Shuigeng Zhou · 2021
Later among the works it cites.
An empirical study of pre-trained vision models on out-of-distribution generalization
Yaodong Yu, Heinrich Jiang, Dara Bahri, Hossein Mobahi, Seungyeon Kim, Ankit Singh Rawat, Andreas Veit, and Yi Ma · 2021
Later among the works it cites.