Designing Deep Networks to Process Other Deep Networks
Designing Deep Networks to Process Other Deep Networks | NVIDIA Technical Blog
“Deep neural networks (DNNs) are the go-to model for learning functions from data, such as image classifiers or language models. In recent years, deep models have become popular for representing the data samples themselves. For example, a deep model can be trained to represent an image, a 3D object, or a scene, an approach called Implicit Neural Representations. (See also Neural Radiance Fields and Instant NGP). Read on for a few examples of performing operations on a pretrained deep model for both DNNs-that-are-functions and DNNs-that-are-data.
Suppose you have a dataset of 3D objects represented using Implicit Neural Representations (INRs) or Neural Radiance Fields (NeRFs). Very often, you may wish to “edit” the objects to change their geometry or fix errors and abnormalities. For example, to remove a handle of a cup or make all car wheels more symmetric than was reconstructed by the NeRF…”