Understanding and Testing Generalization of Deep Networks on
Out-of-Distribution Data

Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data

17 November 2021

Rui Hu

Rui Hu

Papers citing "Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data"

8 / 8 papers shown

Title
Holistic chemical evaluation reveals pitfalls in reaction prediction models Victor Sabanza Gil Andres M Bran Malte Franke Remi Schlama J. Luterbacher Philippe Schwaller ELM 30 1 0 14 Dec 2023
Towards out of distribution generalization for problems in mechanics Lingxiao Yuan Harold S. Park Emma Lejeune OOD AI4CE 36 17 0 29 Jun 2022
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Wei Ma Mike Papadakis Yves Le Traon MQ 41 1 0 08 Apr 2022
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Mike Papadakis Yves Le Traon 23 5 0 08 Apr 2022
A benchmark with decomposed distribution shifts for 360 monocular depth estimation G. Albanis N. Zioulis Petros Drakoulis Federico Álvarez D. Zarpalas P. Daras MDE 40 0 0 01 Dec 2021
Deep Domain-Adversarial Image Generation for Domain Generalisation Kaiyang Zhou Yongxin Yang Timothy M. Hospedales Tao Xiang OOD 215 405 0 12 Mar 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,572 0 17 Apr 2017
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 297 10,225 0 16 Nov 2016