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Found 7 papers, 4 papers with code
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation.
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Ensuring a neural network is not relying on protected attributes (e. g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI.
Shadow Datasets, New challenging datasets for Causal Representation Learning
Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling.
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split.
CAT: Controllable Attribute Translation for Fair Facial Attribute Classification
As the social impact of visual recognition has been under scrutiny, several protected-attribute balanced datasets emerged to address dataset bias in imbalanced datasets.
ULDGNN: A Fragmented UI Layer Detector Based on Graph Neural Networks
While some work attempt to generate front-end code intelligently from UI screenshots, it may be more convenient to utilize UI design drafts in Sketch which is a popular UI design software, because we can access multimodal UI information directly such as layers type, position, size, and visual images.
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition
As equality issues in the use of face recognition have garnered a lot of attention lately, greater efforts have been made to debiased deep learning models to improve fairness to minorities.
