51 citations · 64 across the 17 of their papers we have counts for
8 papers · 1 filter
A Critical Review of Predominant Bias in Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predominant biases is crucial in advanci…
ManiFPT: Defining and Analyzing Fingerprints of Generative Models
Hae Jin Song, Mahyar Khayatkhoei, Wael AbdAlmageed
Recent works have shown that generative models leave traces of their underlying generative process on the generated samples, broadly referred to as fingerprints of a generative mod…
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising meth…
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu +3
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. While several promising met…
Shadow Datasets, New challenging datasets for Causal Representation Learning
Jiageng Zhu, Hanchen Xie, Jianhua Wu +4
Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which i…
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
Mahyar Khayatkhoei, Wael AbdAlmageed
Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models. Given their cen…
Spatial Frequency Bias in Convolutional Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal
As the success of Generative Adversarial Networks (GANs) on natural images quickly propels them into various real-life applications across different domains, it becomes more and mo…
Disconnected Manifold Learning for Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative A…