ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2205.15704
  4. Cited By
Mitigating Dataset Bias by Using Per-sample Gradient
v1v2v3 (latest)

Mitigating Dataset Bias by Using Per-sample Gradient

31 May 2022
Sumyeong Ahn
Seongyoon Kim
Se-Young Yun
ArXiv (abs)PDFHTML

Papers citing "Mitigating Dataset Bias by Using Per-sample Gradient"

50 / 66 papers shown
Title
Learning Debiased Classifier with Biased Committee
Learning Debiased Classifier with Biased Committee
Nayeong Kim
Sehyun Hwang
SungSoo Ahn
Jaesik Park
Suha Kwak
CML
71
57
0
22 Jun 2022
Rich Feature Construction for the Optimization-Generalization Dilemma
Rich Feature Construction for the Optimization-Generalization Dilemma
Jianyu Zhang
David Lopez-Paz
Léon Bottou
42
41
0
24 Mar 2022
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models
UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models
Arvindkumar Krishnakumar
Tong He
Shengji Tang
Judy Hoffman
59
31
0
29 Oct 2021
Simple data balancing achieves competitive worst-group-accuracy
Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi
Martín Arjovsky
Mohammad Pezeshki
David Lopez-Paz
115
182
0
27 Oct 2021
On the Importance of Gradients for Detecting Distributional Shifts in
  the Wild
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang
Andrew Geng
Yixuan Li
276
350
0
01 Oct 2021
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation
Eungyeup Kim
Jihyeon Janel Lee
Jaegul Choo
66
88
0
23 Aug 2021
Unsupervised Learning of Debiased Representations with Pseudo-Attributes
Unsupervised Learning of Debiased Representations with Pseudo-Attributes
Seonguk Seo
Joon-Young Lee
Bohyung Han
FaML
104
50
0
06 Aug 2021
Just Train Twice: Improving Group Robustness without Training Group
  Information
Just Train Twice: Improving Group Robustness without Training Group Information
Emmy Liu
Behzad Haghgoo
Annie S. Chen
Aditi Raghunathan
Pang Wei Koh
Shiori Sagawa
Percy Liang
Chelsea Finn
OOD
94
562
0
19 Jul 2021
Learning Debiased Representation via Disentangled Feature Augmentation
Learning Debiased Representation via Disentangled Feature Augmentation
Jungsoo Lee
Eungyeup Kim
Juyoung Lee
Jihyeon Janel Lee
Jaegul Choo
CML
60
155
0
03 Jul 2021
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised
  Learning
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty
Xujiang Zhao
F. Chen
Rishabh K. Iyer
75
84
0
14 Jun 2021
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Yujia Bao
Shiyu Chang
Regina Barzilay
74
21
0
26 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers
  Solutions with Superior OOD Generalization
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization
Damien Teney
Ehsan Abbasnejad
Simon Lucey
Anton Van Den Hengel
103
90
0
12 May 2021
Discover the Unknown Biased Attribute of an Image Classifier
Discover the Unknown Biased Attribute of an Image Classifier
Zhiheng Li
Chenliang Xu
71
50
0
29 Apr 2021
Explaining in Style: Training a GAN to explain a classifier in
  StyleSpace
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang
Yossi Gandelsman
Michal Yarom
Yoav Wald
G. Elidan
...
William T. Freeman
Phillip Isola
Amir Globerson
Michal Irani
Inbar Mosseri
GAN
112
154
0
27 Apr 2021
Improved and efficient inter-vehicle distance estimation using road
  gradients of both ego and target vehicles
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
Robik Shrestha
Jinkyu Lee
Kushal Kafle
S. Hwang
Il Yong Chun
54
1
0
01 Apr 2021
EnD: Entangling and Disentangling deep representations for bias
  correction
EnD: Entangling and Disentangling deep representations for bias correction
Enzo Tartaglione
C. Barbano
Marco Grangetto
78
124
0
02 Mar 2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient
  Deep Model Training
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty
D. Sivasubramanian
Ganesh Ramakrishnan
A. De
Rishabh K. Iyer
OOD
147
205
0
27 Feb 2021
GLISTER: Generalization based Data Subset Selection for Efficient and
  Robust Learning
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Krishnateja Killamsetty
D. Sivasubramanian
Ganesh Ramakrishnan
Rishabh Iyer University of Texas at Dallas
74
211
0
19 Dec 2020
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations
  in 3D
Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D
Ankit Goyal
Kaiyu Yang
Dawei Yang
Jia Deng
80
42
0
03 Dec 2020
Fair Attribute Classification through Latent Space De-biasing
Fair Attribute Classification through Latent Space De-biasing
V. V. Ramaswamy
Sunnie S. Y. Kim
Olga Russakovsky
57
164
0
02 Dec 2020
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained
  Classification Problems
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
N. Sohoni
Jared A. Dunnmon
Geoffrey Angus
Albert Gu
Christopher Ré
77
252
0
25 Nov 2020
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural
  Networks
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks
L. N. Darlow
Stanisław Jastrzębski
Amos Storkey
124
24
0
19 Nov 2020
Environment Inference for Invariant Learning
Environment Inference for Invariant Learning
Elliot Creager
J. Jacobsen
R. Zemel
OOD
61
383
0
14 Oct 2020
Why resampling outperforms reweighting for correcting sampling bias with
  stochastic gradients
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
Jing An
Lexing Ying
Yuhua Zhu
99
40
0
28 Sep 2020
Swapping Autoencoder for Deep Image Manipulation
Swapping Autoencoder for Deep Image Manipulation
Taesung Park
Jun-Yan Zhu
Oliver Wang
Jingwan Lu
Eli Shechtman
Alexei A. Efros
Richard Y. Zhang
104
335
0
01 Jul 2020
The Pitfalls of Simplicity Bias in Neural Networks
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah
Kaustav Tamuly
Aditi Raghunathan
Prateek Jain
Praneeth Netrapalli
AAML
69
361
0
13 Jun 2020
Generalized ODIN: Detecting Out-of-distribution Image without Learning
  from Out-of-distribution Data
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
Yen-Chang Hsu
Yilin Shen
Hongxia Jin
Z. Kira
OODD
98
573
0
26 Feb 2020
Adversarial Filters of Dataset Biases
Adversarial Filters of Dataset Biases
Ronan Le Bras
Swabha Swayamdipta
Chandra Bhagavatula
Rowan Zellers
Matthew E. Peters
Ashish Sabharwal
Yejin Choi
103
223
0
10 Feb 2020
Don't Judge an Object by Its Context: Learning to Overcome Contextual
  Bias
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Krishna Kumar Singh
D. Mahajan
Kristen Grauman
Yong Jae Lee
Matt Feiszli
Deepti Ghadiyaram
63
109
0
09 Jan 2020
Distributionally Robust Neural Networks for Group Shifts: On the
  Importance of Regularization for Worst-Case Generalization
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Shiori Sagawa
Pang Wei Koh
Tatsunori B. Hashimoto
Percy Liang
OOD
108
1,243
0
20 Nov 2019
Learning De-biased Representations with Biased Representations
Learning De-biased Representations with Biased Representations
Hyojin Bahng
Sanghyuk Chun
Sangdoo Yun
Jaegul Choo
Seong Joon Oh
OOD
382
281
0
07 Oct 2019
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known
  Dataset Biases
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
Christopher Clark
Mark Yatskar
Luke Zettlemoyer
OOD
76
466
0
09 Sep 2019
Invariant Risk Minimization
Invariant Risk Minimization
Martín Arjovsky
Léon Bottou
Ishaan Gulrajani
David Lopez-Paz
OOD
192
2,241
0
05 Jul 2019
RUBi: Reducing Unimodal Biases in Visual Question Answering
RUBi: Reducing Unimodal Biases in Visual Question Answering
Rémi Cadène
Corentin Dancette
H. Ben-younes
Matthieu Cord
Devi Parikh
CML
99
373
0
24 Jun 2019
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash
Chicheng Zhang
A. Krishnamurthy
John Langford
Alekh Agarwal
BDLUQCV
88
776
0
09 Jun 2019
Characterizing Bias in Classifiers using Generative Models
Characterizing Bias in Classifiers using Generative Models
Daniel J. McDuff
Shuang Ma
Yale Song
Ashish Kapoor
64
47
0
30 May 2019
REPAIR: Removing Representation Bias by Dataset Resampling
REPAIR: Removing Representation Bias by Dataset Resampling
Yi Li
Nuno Vasconcelos
FaML
76
287
0
16 Apr 2019
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OODVLM
191
3,445
0
28 Mar 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly
  well on ImageNet
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
Wieland Brendel
Matthias Bethge
SSLFAtt
101
561
0
20 Mar 2019
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
  Classification
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Daniel Borkan
Lucas Dixon
Jeffrey Scott Sorensen
Nithum Thain
Lucy Vasserman
88
491
0
11 Mar 2019
Learning Robust Representations by Projecting Superficial Statistics Out
Learning Robust Representations by Projecting Superficial Statistics Out
Haohan Wang
Zexue He
Zachary Chase Lipton
Eric Xing
OOD
70
235
0
02 Mar 2019
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Learning Not to Learn: Training Deep Neural Networks with Biased Data
Byungju Kim
Hyunwoo Kim
Kyungsu Kim
Sungjin Kim
Junmo Kim
OOD
59
410
0
26 Dec 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras
S. Laine
Timo Aila
593
10,561
0
12 Dec 2018
Deep Learning for Classical Japanese Literature
Deep Learning for Classical Japanese Literature
Tarin Clanuwat
Mikel Bober-Irizar
A. Kitamoto
Alex Lamb
Kazuaki Yamamoto
David R Ha
108
710
0
03 Dec 2018
ImageNet-trained CNNs are biased towards texture; increasing shape bias
  improves accuracy and robustness
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos
Patricia Rubisch
Claudio Michaelis
Matthias Bethge
Felix Wichmann
Wieland Brendel
115
2,672
0
29 Nov 2018
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep
  Neural Network Embeddings
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
Mohsan S. Alvi
Andrew Zisserman
C. Nellåker
FaML
123
248
0
06 Sep 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with
  Noisy Labels
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang
M. Sabuncu
NoLa
85
2,608
0
20 May 2018
A Reductions Approach to Fair Classification
A Reductions Approach to Fair Classification
Alekh Agarwal
A. Beygelzimer
Miroslav Dudík
John Langford
Hanna M. Wallach
FaML
227
1,102
0
06 Mar 2018
Not All Samples Are Created Equal: Deep Learning with Importance
  Sampling
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos
François Fleuret
95
522
0
02 Mar 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension
  Reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
187
9,460
0
09 Feb 2018
12
Next