Aug 19, 2019 · The BigGAN is an implementation of the GAN architecture designed to leverage the best from what has been reported to work more generally. It was described by Andrew Brock, et al. in their 2018 paper titled “ Large Scale GAN Training for High Fidelity Natural Image Synthesis ” and presented at the ICLR 2019 conference.

Jan 26, 2024 · See the BigGAN paper on arXiv [1] for more information about these models. After connecting to a runtime, get started by following these instructions: (Optional) Update the selected module_path in the first code cell below to load a BigGAN generator for a different image resolution. Click Runtime > Run all to run each cell in order.

Sep 28, 2018 · To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale.

Mar 21, 2019 · This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue and Karen Simonyan.

BigGAN is a type of generative adversarial network that was designed for scaling generation to high-resolution, high-fidelity images. It includes a number of incremental changes and innovations. The baseline and incremental changes are: Using SAGAN as a baseline with spectral norm. for G and D, and using TTUR. Using a Hinge Loss GAN objective

This repo contains code for 4-8 GPU training of BigGANs from Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue, and Karen Simonyan.

After connecting to a runtime, get started by following these instructions: (Optional) Update the selected module_path in the first code cell below to load a BigGAN generator for a different...

BigGAN-deep is a deeper version (4x) of BigGAN. The main difference is a slightly differently designed residual block. Here the $z$ vector is concatenated with the conditional vector without splitting it into chunks. It is also based on residual blocks with bottlenecks.

Mar 25, 2021 · Paper of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS. Examples of 512x512 images generated by the proposed method. WOW! Such great images with high diversity and resolution...

Oct 2, 2018 · When trained on the ImageNet dataset at 128×128 resolution, BigGAN can achieve an Inception Score (IS) of 166.3, a more than 100 percent improvement over the previous state of the art (SotA)...