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Accuracy on the Line: On the Strong Correlation Between
  Out-of-Distribution and In-Distribution Generalization
v1v2 (latest)

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

International Conference on Machine Learning (ICML), 2021
9 July 2021
John Miller
Rohan Taori
Aditi Raghunathan
Shiori Sagawa
Pang Wei Koh
Vaishaal Shankar
Abigail Z. Jacobs
Y. Carmon
Ludwig Schmidt
    OODDOOD
ArXiv (abs)PDFHTML

Papers citing "Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization"

13 / 213 papers shown
Title
Failure Modes of Domain Generalization Algorithms
Failure Modes of Domain Generalization AlgorithmsComputer Vision and Pattern Recognition (CVPR), 2021
T. Galstyan
Hrayr Harutyunyan
Hrant Khachatrian
Greg Ver Steeg
Aram Galstyan
OOD
150
13
0
26 Nov 2021
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIPNeural Information Processing Systems (NeurIPS), 2021
Andreas Fürst
Elisabeth Rumetshofer
Johannes Lehner
Viet-Hung Tran
Fei Tang
...
David P. Kreil
Michael K Kopp
Günter Klambauer
Angela Bitto-Nemling
Sepp Hochreiter
VLMCLIP
574
116
0
21 Oct 2021
On the Robustness of Reading Comprehension Models to Entity Renaming
On the Robustness of Reading Comprehension Models to Entity Renaming
Jun Yan
Yang Xiao
Sagnik Mukherjee
Bill Yuchen Lin
Robin Jia
Xiang Ren
266
21
0
16 Oct 2021
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias
Ting-Rui Chiang
96
6
0
11 Oct 2021
Exploring the Limits of Large Scale Pre-training
Exploring the Limits of Large Scale Pre-training
Samira Abnar
Mostafa Dehghani
Behnam Neyshabur
Hanie Sedghi
AI4CE
201
133
0
05 Oct 2021
Robust fine-tuning of zero-shot models
Robust fine-tuning of zero-shot models
Mitchell Wortsman
Gabriel Ilharco
Jong Wook Kim
Mike Li
Simon Kornblith
...
Raphael Gontijo-Lopes
Hannaneh Hajishirzi
Ali Farhadi
Hongseok Namkoong
Ludwig Schmidt
VLM
589
880
0
04 Sep 2021
Towards Out-Of-Distribution Generalization: A Survey
Towards Out-Of-Distribution Generalization: A Survey
Tianyu Wang
Zheyan Shen
Yue He
Xingxuan Zhang
Renzhe Xu
Han Yu
Peng Cui
CMLOOD
468
626
0
31 Aug 2021
SHIFT15M: Fashion-specific dataset for set-to-set matching with several
  distribution shifts
SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
Masanari Kimura
Takuma Nakamura
Yuki Saito
OOD
190
3
0
30 Aug 2021
Dangers of Bayesian Model Averaging under Covariate Shift
Dangers of Bayesian Model Averaging under Covariate ShiftNeural Information Processing Systems (NeurIPS), 2021
Pavel Izmailov
Patrick K. Nicholson
Sanae Lotfi
A. Wilson
OODUQCVBDL
321
48
0
22 Jun 2021
If your data distribution shifts, use self-learning
If your data distribution shifts, use self-learning
E. Rusak
Steffen Schneider
George Pachitariu
L. Eck
Peter V. Gehler
Oliver Bringmann
Wieland Brendel
Matthias Bethge
VLMOODTTA
380
35
0
27 Apr 2021
An overview of artificial intelligence techniques for diagnosis of
  Schizophrenia based on magnetic resonance imaging modalities: Methods,
  challenges, and future works
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Delaram Sadeghi
A. Shoeibi
Navid Ghassemi
Parisa Moridian
Ali Khadem
...
Juan M Gorriz
F. Khozeimeh
Yu-Dong Zhang
S. Nahavandi
U. Acharya
396
120
0
24 Feb 2021
Unsupervised Learning of Visual Features by Contrasting Cluster
  Assignments
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron
Ishan Misra
Julien Mairal
Priya Goyal
Piotr Bojanowski
Armand Joulin
OCLSSL
1.2K
4,626
0
17 Jun 2020
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable ConvolutionsComputer Vision and Pattern Recognition (CVPR), 2016
François Chollet
MDEBDLPINN
2.8K
16,539
0
07 Oct 2016
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