Bias in Generative Models
This project explores the case for potential bias in generative models such as variational autoencoders. The project also briefly looks at ways to mitigate such bias.
Using VAE for Robustness
Exploring potential use cases of variational autoencoders in the context of robustness of ML systems.
Adversarial Attacks and Defense for CNN
The project explores CNN classification models and their vulnerability to various adversarial attacks. Also explores a defence mechanism for it.
Exploring Personalized Image Captioning
This project explores personalization of generating image captions. We explore different architecture choices of Attend2u and also analyze personalization of the captions.