42 citations · 57 across the 14 of their papers we have counts for
6 papers · 1 filter
Reinforcement Learning of Self Enhancing Camera Image and Signal Processing
Chandrajit Bajaj, Yi Wang, Yunhao Yang
Current camera image and signal processing pipelines (ISPs), including deep-trained versions, tend to apply a single filter that is uniformly applied to the entire image. This is d…
Recipes for when Physics Fails: Recovering Robust Learning of Physics Informed Neural Networks
Chandrajit Bajaj, Luke McLennan, Timothy Andeen +1
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the tra…
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Qixing Huang, Xiangru Huang, Bo Sun +3
This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated s…
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
Haitao Yang, Zaiwei Zhang, Siming Yan +5
Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as i…
Deep Predictive Learning of Carotid Stenosis Severity
Yiqun Diao, Oliver Zhao, Priya Kothapalli +2
Carotid artery stenosis is the narrowing of carotid arteries, which supplies blood to the neck and head. In this work, we train a model to predict the severity of the stenosis bloc…
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing
Yunhao Yang, Yi Wang, Chandrajit Bajaj
Camera Image Signal Processing (ISP) pipelines can get appealing results in different image signal processing tasks. Nonetheless, the majority of these methods, including those emp…