Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural
Networks

Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks

19 November 2020

Stanisław Jastrzębski

Amos Storkey

Papers citing "Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks"

7 / 7 papers shown

Title
Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair Jeonghoon Park Chaeyeon Chung Juyoung Lee Jaegul Choo 32 2 0 30 Apr 2024
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels Sumyeong Ahn Se-Young Yun NoLa 19 2 0 01 Dec 2022
Revisiting the Importance of Amplifying Bias for Debiasing Jungsoo Lee Jeonghoon Park Daeyoung Kim Juyoung Lee E. Choi Jaegul Choo 37 21 0 29 May 2022
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation Eungyeup Kim Jihyeon Janel Lee Jaegul Choo 17 86 0 23 Aug 2021
Learning Debiased Representation via Disentangled Feature Augmentation Jungsoo Lee Eungyeup Kim Juyoung Lee Jihyeon Janel Lee Jaegul Choo CML 12 148 0 03 Jul 2021
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 262 10,320 0 12 Dec 2018
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018