Search Results for author:
Found 6 papers, 1 papers with code
Practical Bias Mitigation through Proxy Sensitive Attribute Label Generation
Addressing bias in the trained machine learning system often requires access to sensitive attributes.
GroupMixNorm Layer for Learning Fair Models
Analysis on benchmark tabular and image datasets demonstrates the efficacy of the proposed method in achieving state-of-the-art performance.
Transitioning from Real to Synthetic data: Quantifying the bias in model
This study aims to establish a trade-off between bias and fairness in the models trained using synthetic data.
Towards Zero-Shot Learning with Fewer Seen Class Examples
We present a meta-learning based generative model for zero-shot learning (ZSL) towards a challenging setting when the number of training examples from each \emph{seen} class is very few.
Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval
Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training.
Blind Deblurring Using GANs
Modifications to the structures is done to improve the global perception of the model.
