Overview of BigGAN
BigGAN is a GAN (Generative Adversarial Network) proposed by researchers at Google DeepMind that is capable of generating high-resolution and high-quality images, especially by training on large data sets (such as ImageNet) and using larger batch sizes than conventional GANs as described in “Overview of GANs and Various Applications and Implementations”, BigGAN is capable of generating high-definition images by using a larger batch size than conventional GANs.
The features of BigGAN are as follows.
(1) Spectral Normalization
- Similar to SNGAN (Spectral Normalization GAN), spectral normalization is applied to both the discriminator (Discriminator) and the generator (Generator) to improve learning stability.
- Spectral normalization strengthens the Lipschitz constraints on the discriminator and prevents gradient loss and divergence.
(2) Large Batch Training
- When using a large dataset such as ImageNet, it is possible to improve the quality of generation by increasing the batch size.
- In our research, we trained with batch size = 2048 and achieved smoother and higher quality image generation than conventional GAN.
(3) Class-Conditional Generation
- Class information can be added as input to generate images of specific categories (e.g., dogs, cars, flowers, etc.).
- By combining embedding vectors and Adaptive Instance Normalization (AdaIN), more diverse images can be generated.
(4) Truncation Trick (truncation technique)
- Improves image quality by limiting latent variables (noise).
- Normally, GAN takes a random noise vector as input, but BigGAN can generate clearer images by restricting the distribution of noise.
The architecture of BigGAN is composed of the following elements
(1) Generator
- Convolutional blocks (ResNet-based) are used to improve learning stability.
- Adaptive Batch Normalization (AdaBN) is used to incorporate class information.
- Utilizes latent variable truncation (Truncation Trick).
(2) Discriminator
- Stabilizes training of the discriminator by using Spectral Normalization.
- A simple convolutional network is used to extract features.
The challenges of BigGAN are as follows
- High learning cost
- Requires a large amount of computing resources (TPU, GPU).
- Increasing batch size improves performance, but increases computational load.
- Risk of Mode Collapse
- It may be difficult to generate a variety of images (convergence to a specific pattern).
- Using Truncation Trick improves quality but reduces diversity.
Countermeasures against them include the following.
- The StyleGAN series (StyleGAN2, StyleGAN3) offers finer control than BigGAN.
- BigGAN + Diffusion Models (combined with diffusion models) is attracting attention as a new image generation technique.
BigGAN is a large-scale GAN that extends SNGAN technology described in “Overview of SNGAN (Spectral Normalization GAN), algorithms and implementation examples” and is capable of generating high-resolution images, especially the combination of “Truncation Trick” and “Spectral Normalization” methods.
Implementation Example
This section describes how to generate images with BigGAN using PyTorch.
1. environment setup: To use BigGAN, you need to install pytorch-pretrained-biggan.
install necessary libraries
pip install torch torchvision numpy pillow pytorch-pretrained-biggan2. Image generation using BigGAN: The following code generates images corresponding to a specific class (e.g., dog).
Model loading & image generation
import torch
from pytorch_pretrained_biggan import BigGAN, truncated_noise_sample
from PIL import Image
import numpy as np
# 1. load BigGAN model (ImageNet 256x256 version)
model = BigGAN.from_pretrained('biggan-deep-256')
# 2. class of image to be generated (e.g., dog = 207)
class_vector = torch.zeros((1, 1000))
class_vector[0, 207] = 1 # 207 は "Golden Retriever"
# 3. applying truncation technique (truncation=0.5)
noise_vector = truncated_noise_sample(truncation=0.5, batch_size=1)
noise_vector = torch.from_numpy(noise_vector)
# 4. image generation with BigGAN
with torch.no_grad():
output = model(noise_vector, class_vector, truncation=0.5)
# 5. convert and save the image
output_image = (output.cpu().numpy().squeeze() + 1) / 2 # Converted to 0-1 scale
output_image = np.transpose(output_image, (1, 2, 0)) # CHW → HWC
output_image = (output_image * 255).astype(np.uint8)
Image.fromarray(output_image).save("biggan_output.jpg")In this code, the image with class ID 207 (Golden Retriever) is raw and saved as “biggan_output.jpg”.
3. Additional Tuning
Generating images of other classes: By changing the ImageNet class IDs (e.g., cat, car, bird), different images can be generated. A list of class IDs can be found on the official ImageNet website.
class_vector[0, 281] = 1 # 281 は "猫（Tabby cat）"Adjustment of truncation parameters: Changing truncation=0.5 to 0.8 or 1.0 can generate a variety of images, and 0.3 or lower improves quality but reduces variation in the generated images.
truncation = 0.8
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1)Application Examples
BigGAN is a GAN that excels in high-resolution image generation and is used in a variety of fields. Specific applications are described below. 1.
1. creative and artistic fields : BigGAN is used for automatic generation of artworks and design support because of its ability to generate high quality images.
- Case study: Generation of GAN art
- Artworks using BigGAN have been presented in “AI Art Contest” and other competitions.
- Combined with style fusion (Style Transfer), it generates artwork with a new style of painting.
- In some cases, it is applied to the generation of animation and illustrations.
- Examples:
- Artbreeder: A creative platform that uses BigGAN to synthesize faces and landscapes.
- GANPaint Studio: A tool that enables editing such as “adding trees” and “removing windows” using BigGAN. 2.
2. game and CG production: In the game industry, realistic background and character generation using BigGAN is being researched.
- Case study: Automatic generation of game assets
- Automatic generation of background textures (desert, forest, city, etc.)
- Automatic generation of character design candidates
- Increased variation of enemy characters
- Examples:
- Ubisoft: BigGAN is used as part of research into automatic map generation using AI.
- NVIDIA GauGAN: tool to generate landscape images from sketches.
3. fashion design: BigGAN is also used to generate new design ideas.
- Case study: designing clothes, shoes, and accessories
- AI-based fashion design proposal
- Learns from past designs and automatically generates new styles
- Output designs that match brand concepts
- Examples:
- Zalando Research: Utilized GAN for AI-based fashion design research.
- Nike: Tried to generate their own sneaker designs using GAN.
4. medical and biotech: BigGAN can be applied to medical image generation and data completion.
- Case study: Generation and completion of pathology images
- AI generates realistic data when medical data is scarce
- Synthesize images of cancer cells and lesions to improve diagnostic accuracy
- Higher resolution MRI and CT scans
- Examples:
- Google Health: research on medical image synthesis using GAN.
- Stanford University: Developed noise reduction technology for medical images using GAN.
5. advertising and marketing: In the marketing field, BigGAN is used for automatic generation of images for advertisements.
- Case study: AI-based ad visual generation
- Optimization of visuals according to consumer preferences
- Automatic generation of personalized ad designs by AI
- Creation of ad materials by generating realistic images of people
- Examples:
- This Person Does Not Exist (a website that generates fictitious person images)
- AdCreative.ai (automatic generation of ad materials using AI)
6. science and research: BigGAN is also used as a research aid in areas where data is scarce.
- Case study: Generation of new materials and chemical structures
- Generation of new molecular structures (drug discovery using GAN)
- Synthesis of artificial astronomical images (space simulation)
- Complementation and simulation of meteorological data
- Examples:
- MIT: Design of new molecular structures using GAN.
- NASA: Analysis of astronomical data using GAN.
reference book
The following is a list of reference books related to BigGAN.
Basics of GAN
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
Author: David Foster
Publication Year: 2019
DESCRIPTION: Provides a wide range of examples of implementations of GANs, from the basics to applications; also discusses BigGANs.
URL: O’Reilly
Deep Learning for Computer Vision with Python
Author: Adrian Rosebrock
Publication Year: 2019
DESCRIPTION: Provides extensive knowledge of image generation AI and computer vision, including examples of GAN applications.
‘Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence‘
Author: Jon Krohn
Publication Year: 2019
Description: a visual guide to deep learning, including GANs. Easy to understand for beginners.
Applications and Developments of GANs
‘GANs in Action: Deep Learning with Generative Adversarial Networks‘
Author(s): Jakub Langr, Vladimir Bok
Publication Year: 2019
Description: details the theory, implementation, and latest research trends in GANs; includes methods related to BigGANs
‘Hands-On Generative Adversarial Networks with PyTorch 1.x‘
Author: John Hany, Greg Walters
Publication year: 2020
Description: Provides a detailed description of how to implement GANs using PyTorch and is useful for BigGAN implementations.
Advanced Deep Learning with TensorFlow 2 and Keras
Author: Rowel Atienza
Publication year: 2020
Description: Explains the latest GAN technologies (BigGAN, StyleGAN, CycleGAN, etc.).
Theory and Implementation of BigGAN
Large Scale GAN Training for High Fidelity Natural Image Synthesis” (BigGAN paper)
Author(s): Andrew Brock, Jeff Donahue, Karen Simonyan
Publication Year: 2018
URL: PDF of the paper
DESCRIPTION: Provides a detailed description of the theory and experimental results of BigGAN.
The Generative Adversarial Networks Cookbook
Author: Josh Kalin
Publication Year: 2018
Description: describes various applications of GANs, including BigGAN.
Resources for learning to implement BigGAN
GitHub repository
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