Generative Models as Distributions of Functions

 Cool new paper on rethinking generative models as building a resolution independent functional representation.  So your representation is scale independent.

You can use the same architecture for generating images or 3D data or audio data.

They also use the same trick described in yesterday's NeRF post where they use random Fourier features to enable the model to represent high frequency data effectively.

Here's a link to the paper 'Generative models as Distributions of Functions'.

Here's a link to the PyTorch code for the paper.

JaeJun Yoo put together a nice set of slides for a talk that covers this material here.


Popular posts from this blog

Simulating the Universe with Machine Learning

CycleGAN: a GAN architecture for learning unpaired image to image transformations

Pix2Pix: a GAN architecture for image to image transformation