15 citations · 25 across the 10 of their papers we have counts for
10 papers
Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
Malick Ebiele, Malika Bendechache, Rob Brennan
Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications. To validate the usefulness of a new method, it needs to be compared agai…
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…
Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method
Malick Ebiele, Malika Bendechache, Eamonn Clinton +1
In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has…
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…
AudRandAug: Random Image Augmentations for Audio Classification
Teerath Kumar, Muhammad Turab, Alessandra Mileo +2
Data augmentation has proven to be effective in training neural networks. Recently, a method called RandAug was proposed, randomly selecting data augmentation techniques from a pre…
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…
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
Sidra Aleem, Teerath Kumar, Suzanne Little +3
Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learn…
Investigating Multi-Feature Selection and Ensembling for Audio Classification
Muhammad Turab, Teerath Kumar, Malika Bendechache +1
Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some int…