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A deep learning solution using Siamese networks to solve the problem of face verification for an NGO. This was part of a winning solution for a competition held by Mastek. Competition link -
In this project new masking strategies are proposed for more competitive MIM-based self-supervised learning. Furthermore, a new loss function, based on contrastive learning, is introduced and achieves improvements over the baseline when used with different masking strategies.
This project aims to build a fashion similarity model using TensorFlow MNIST Datasets and Siamese Network with custom model and contrastive loss. The model can compare two images of clothing items and determine how similar they are.
A deep learning solution using Siamese networks to solve the problem of face verification for an NGO. This was part of a winning solution for a competition held by Mastek. Competition link -
COMPSCI 696DS Industry Mentorship Program with Meta Reality Labs: Ambient AI: Multimodal Wearable Sensor Understanding (Experiments in Distilling Knowledge in Cross-Modal Contrastive Learning.)
Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.