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Uncovering bias and uncertainty in model using Semi-Supervised VAEs
[code] [blog]
This project aims to investigate and quantify the biases present in face detection models. Identified biases include a preference for white faces over black faces, higher accuracy in detecting male faces compared to female faces, better detection of faces without glasses, and variations in accuracy based on different hair colors. The ultimate goal is to highlight these biases and suggest ways to mitigate them, promoting the development of fairer and more inclusive face detection systems.
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Writer independent offline handwriting verification
[code] [blog]
Developed a model using PyTorch CRAFT and Vision Transformer to determine if two handwritten Hindi images are by the same writer. Achieved an AUC of 0.72 and 10th place in a NCVPRIPG workshop competition.
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Nature Inspired Neural Networks
[code]
Optimizing neural network weights using nature-inspired algorithms instead of gradient descent and backpropagation. The algorithms include Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm.
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More projects can be found on Github