This repository provides a Chainer implementation of Generative Adversarial Networks (GANs) enhanced with spectral normalization and a projection discriminator. It targets researchers and practitioners in deep learning and computer vision interested in advanced conditional image generation techniques, offering improved stability and sample quality.

How It Works

The project leverages spectral normalization in the discriminator to constrain the Lipschitz constant of the network, promoting training stability. The projection discriminator further enhances conditional generation by incorporating class information directly into the discriminator's decision-making process, leading to more accurate and class-conditional image synthesis.

Quick Start & Requirements

• Install required libraries: pip install -r requirements.txt
• Requires ImageNet (ILSVRC2012) dataset.
• Preprocessing script datasets/preprocess.sh is provided.
• Training requires a single GPU.
• Links to demo movies and generated images are available in the README.

Highlighted Details

• Implements spectral normalization and projection discriminator for GANs.
• Supports conditional image generation on ImageNet.
• Provides scripts for training, image generation, category morphing, and inception score calculation.
• Offers pretrained models for ImageNet and dog/cat datasets.

Maintenance & Community

The project is associated with pfnet-research. No specific community channels or roadmap are detailed in the README.

Licensing & Compatibility

The license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The provided setup and example code are for single-GPU training with smaller models than those used in the referenced papers. Reproducing the exact paper results may require different configurations or hardware. The project appears to be based on Chainer, which has reached its end-of-life.