A Supervised Generative Model for Efficient Rendering of Medical Volume Datadoi:10.1109/EHB50910.2020.9279880Isosurfaces,Rendering (computer graphics),Neural networks,Training,Data models,Task analysis,Biomedical imagingComplex 3D and multidimensional medical data require significant computational resources to ...
Unlike other methods that use low entropy wide-field images, this approach takes full advantage of the high entropy properties of structured illumination images to train a CNN model. Optical-sectioning imaging using this method only requires a single image for decoding, thereby improving the raw ima...
Furthermore, in addition to generating drug-like molecules, an effective molecule generative model should be capable of generating active compounds. Since it is impractical to synthesize all generated molecules for real-world testing, an alternative approach is to evaluate whether the model can reproduc...
Tailor - Cross-platform static analyzer for Swift that helps you to write cleaner code and avoid bugs. WeakableSelf - A Swift micro-framework to encapsulate [weak self] and guard statements within closures.back to topLinterStatic code analyzers to enforce style and conventions.Any...
It supports multi-scale 3D face geometry estimation, high-quality portrait relighting, and free-viewpoint rendering. We employ a parametric-neural model to account for shape estimation, neural relighting, and implicit deep material modelling under a differentiable rendering pipeline. More importantly, ...
Finally, we compute the likelihood term by comparing these rendered results from the generative model with the directly estimated results calculated by the discriminative models. Specifically, the likelihood is computed by the pixel-wise differences between the two sets of maps, ...
Results Generative model of causes and observables. Many every-day decisions are based on inferences about hidden causes from ambiguous and noisy observations. Consider inferring the cause of observing a wet pavement in the morning. Many events could have caused it being wet, such as rain ...
Transfer learning. To build a well-performing model from limited known active compounds (target data, such as RIPK1 inhibitors here), we applied transfer learning13,24during the training process (Fig.2b). For general optimization, we pre-trained the generative model using a large-scale dataset ...
The results obtained from low level are used for high level analysis that is event detection. The architecture proposed in the model includes five main modules. The five sections are Event model learning Action model learning Action detection Complex event model learning Complex event detection...
A model scanned by photogrammetry for the Digital Emily project [4] Full size image 4.23D morphable models and generative adversarial networks 3D face reconstruction Face reconstruction is the estimation from single images of the facial shape, texture, and other intrinsic components, such as albedo ...