Search Results for author:
Found 5 papers, 2 papers with code
Revealing the Utilized Rank of Subspaces of Learning in Neural Networks
In our findings, we conclude that most models utilize a fraction of the available space.
GAUDI: A Neural Architect for Immersive 3D Scene Generation
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
Fast and Explicit Neural View Synthesis
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
Learning to Branch for Multi-Task Learning
Training multiple tasks jointly in one deep network yields reduced latency during inference and better performance over the single-task counterpart by sharing certain layers of a network.
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric.
Ranked #19 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
