Across evolutionary history, every organism has developed the ability to adapt to fluctuations in environmental conditions, thereby striking a balance between efficient growth and survival. In the case of yeast, when exposed ...

This Thesis focuses on combining the two well researched concepts of representation learning – Dictionary Learning and Deep Learning. These two learning paradigms have been known for long. Ever since, plethora of papers ...

With the increased interest in face recognition across di erent applications, the research in this area has ourished over the past few decades. However, face recognition with disguise variations has gained little ...

Recent advancement in deep learning techniques has opened doors for wide variety of applications. With growing interests in deep learning and geometry, lots of computer vision problems have been tackled using deep learning. ...

The first part of this work focuses on facial attribute prediction using a novel deep learning formulation, termed as R-Codean autoencoder. The work presents Cosine similarity based loss function in an autoencoder which ...

In the ever-evolving landscape of medical innovation, the pursuit of accurate and efficient diagnostic tools for neurodegenerative diseases, notably Alzheimer's, has become increasingly urgent. The relentless progression ...

We use Mean Shift clustering in the latent space of an auto-encoder to have a better representation of the data and a more structured latent space. Instead of just using the mode of the distribution calculated using kernel ...

Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network ...

Wireless devices, including mobile phones, laptops, autonomous driving, UAT, etc., generate an enormous amount of local data, which could be employed to train machine learning models for decision-making and improve prediction ...

This project focused on the deployment of a deep learning model for species categorization and retraining on the cloud using the YOLOv5x architecture. The goal was to create a web application that allows users to upload ...