46 citations · 48 across the 4 of their papers we have counts for
5 papers
Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention
Jeonghoon Park, Juyoung Lee, Chaeyeon Chung +3
Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text. However, they often exhibit socie…
Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair
Jeonghoon Park, Chaeyeon Chung, Juyoung Lee +1
In the image classification task, deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias, result…
Improving Evaluation of Debiasing in Image Classification
Jungsoo Lee, Juyoung Lee, Sanghun Jung +1
Image classifiers often rely overly on peripheral attributes that have a strong correlation with the target class (i.e., dataset bias) when making predictions. Due to the dataset b…
Revisiting the Importance of Amplifying Bias for Debiasing
Jungsoo Lee, Jeonghoon Park, Daeyoung Kim +3
In image classification, "debiasing" aims to train a classifier to be less susceptible to dataset bias, the strong correlation between peripheral attributes of data samples and a t…
Learning Debiased Representation via Disentangled Feature Augmentation
Jungsoo Lee, Eungyeup Kim, Juyoung Lee +2
Image classification models tend to make decisions based on peripheral attributes of data items that have strong correlation with a target variable (i.e., dataset bias). These bias…