Machine Learning for Autonomous Driving
Nupur Kumari
Mayank Singh
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Abhishek Sinha I am currently a Software Engineer in Waymo under Perception Team, where I am working on Active Learning related projects. I graduated with a Master's degree in the department of Computer Science at Stanford University in 2021. I am interested in the domain of computer vision and deep learning. I am specifically interested in topics such as generative models, anomaly detection, self-supervised learning and active learning. During my Masters, I was a Research Assistant under Professor Stefano
Comparing Distributions by Measuring Differences that Affect Decision Making Proposed a way to measure the discrepancy between two probability distributions based on optimal decision loss. Our approach outperformed prior approaches for two-sample tests across different datasets. The proposed divergence can also be used for feature selection, sample quality evaluation or even studying the effect of climate change. Best Paper Award at ICLR, 2022 .
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation Developed a single model that can both learn rich latent representations, and sample images from that latent space. Added contrastive loss on top of VAE to learn good representations and learnt a strong prior over the latent space of VAE, using diffusion models. Our model allows us to perform few shot conditional generation tasks, such as conditional image manipulation with limited examples. Paper accepted at NeurIPS, 2021 .
Machine Learning for Autonomous Driving
Nupur Kumari
Mayank Singh
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation