Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

11 September 2021

Mobarakol Islam

Lalithkumar Seenivasan

Hongliang Ren

Ben Glocker

ArXiv PDF HTML

Papers citing "Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition"

25 / 25 papers shown

Title
CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation Vishal Thengane Jean Lahoud Hisham Cholakkal Rao Muhammad Anwer L. Yin Xiatian Zhu Salman Khan CLL 389 0 0 24 Feb 2025
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study Zhiqiang Shen Zechun Liu Dejia Xu Zitian Chen Kwang-Ting Cheng Marios Savvides 38 76 0 01 Apr 2021
Local Temperature Scaling for Probability Calibration Zhipeng Ding Xu Han Peirong Liu Marc Niethammer 50 78 0 12 Aug 2020
Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren Cunjun Yu Shunan Sheng Xiao Ma Haiyu Zhao Shuai Yi Hongsheng Li 198 560 0 21 Jul 2020
Learning and Reasoning with the Graph Structure Representation in Robotic Surgery Mobarakol Islam Seenivasan Lalithkumar Lim Chwee Ming Hongliang Ren 38 39 0 07 Jul 2020
Calibrating Deep Neural Networks using Focal Loss Jishnu Mukhoti Viveka Kulharia Amartya Sanyal Stuart Golodetz Philip Torr P. Dokania UQCV 81 454 0 21 Feb 2020
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning Arsenii Ashukha Alexander Lyzhov Dmitry Molchanov Dmitry Vetrov UQCV FedML 69 315 0 15 Feb 2020
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration Meelis Kull Miquel Perelló Nieto Markus Kängsepp Telmo de Menezes e Silva Filho Hao Song Peter A. Flach UQCV 63 378 0 28 Oct 2019
$Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network$ Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network Bin Dong Jikai Hou Yiping Lu Zhihua Zhang 54 41 0 02 Oct 2019
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference M. Laves Sontje Ihler Karl-Philipp Kortmann T. Ortmaier UQCV 39 57 0 30 Sep 2019
Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques Byeongmoon Ji Hyemin Jung Jihyeun Yoon Kyungyul Kim Younghak Shin UQCV 44 24 0 30 Aug 2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao Colin Wei Adrien Gaidon Nikos Arechiga Tengyu Ma 97 1,583 0 18 Jun 2019
When Does Label Smoothing Help? Rafael Müller Simon Kornblith Geoffrey E. Hinton UQCV 154 1,931 0 06 Jun 2019
Large-Scale Long-Tailed Recognition in an Open World Ziwei Liu Zhongqi Miao Xiaohang Zhan Jiayun Wang Boqing Gong Stella X. Yu 134 1,148 0 10 Apr 2019
Measuring Calibration in Deep Learning Jeremy Nixon Michael W. Dusenberry Ghassen Jerfel Timothy Nguyen Jeremiah Zhe Liu Linchuan Zhang Dustin Tran UQCV 67 484 0 02 Apr 2019
What is the Effect of Importance Weighting in Deep Learning? Jonathon Byrd Zachary Chase Lipton 85 458 0 08 Dec 2018
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks A. Mozafari H. Gomes Wilson Leão Steeven Janny Christian Gagné 47 26 0 27 Oct 2018
Deep Imbalanced Learning for Face Recognition and Attribute Prediction Chen Huang Yining Li Chen Change Loy Xiaoou Tang CVBM 45 310 0 01 Jun 2018
Moonshine: Distilling with Cheap Convolutions Elliot J. Crowley Gavia Gray Amos Storkey 53 121 0 07 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 258 9,687 0 25 Oct 2017
A systematic study of the class imbalance problem in convolutional neural networks Mateusz Buda A. Maki Maciej A. Mazurowski 156 2,335 0 15 Oct 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 226 5,774 0 14 Jun 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 290 4,620 0 10 Nov 2016
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 290 19,523 0 09 Mar 2015
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 158 2,114 0 21 Dec 2013