82 citations · 220 across the 34 of their papers we have counts for
16 papers · 1 filter
Physically Disentangled Representations
Tzofi Klinghoffer, Kushagra Tiwary, Arkadiusz Balata +2
State-of-the-art methods in generative representation learning yield semantic disentanglement, but typically do not consider physical scene parameters, such as geometry, albedo, li…
Learning to Censor by Noisy Sampling
Ayush Chopra, Abhinav Java, Abhishek Singh +2
Point clouds are an increasingly ubiquitous input modality and the raw signal can be efficiently processed with recent progress in deep learning. This signal may, often inadvertent…
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma +4
Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assu…
ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
Vivek Sharma, Praneeth Vepakomma, Tristan Swedish +3
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al.…
Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning
Ilke Demir, Ramesh Raskar
More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like…
Flash Photography for Data-Driven Hidden Scene Recovery
Matthew Tancik, Guy Satat, Ramesh Raskar
Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these ta…