15 citations · 25 across the 10 of their papers we have counts for
5 papers · 1 filter
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity
Teerath Kumar, Alessandra Mileo, Malika Bendechache
Data augmentation has become a pivotal tool in enhancing the performance of computer vision tasks, with the KeepOriginalAugment method emerging as a standout technique for its inte…
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation
Teerath Kumar, Alessandra Mileo, Malika Bendechache
Geographical, gender and stereotypical biases in computer vision models pose significant challenges to their performance and fairness. {In this study, we present an approach named…
OxML Challenge 2023: Carcinoma classification using data augmentation
Kislay Raj, Teerath Kumar, Alessandra Mileo +1
Carcinoma is the prevailing type of cancer and can manifest in various body parts. It is widespread and can potentially develop in numerous locations within the body. In the medica…
KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach
Teerath Kumar, Alessandra Mileo, Malika Bendechache
Advanced image data augmentation techniques play a pivotal role in enhancing the training of models for diverse computer vision tasks. Notably, SalfMix and KeepAugment have emerged…
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
Teerath Kumar, Alessandra Mileo, Rob Brennan +1
Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, wher…