This repository provides code for implicit generation and generalization using Energy-Based Models (EBMs). It is targeted at researchers and practitioners in deep learning and generative modeling who are interested in exploring novel approaches to data generation and understanding model generalization capabilities. The primary benefit is the ability to train and evaluate EBMs on various datasets, including CIFAR-10 and ImageNet, with options for conditional generation and out-of-distribution testing.

How It Works

The project implements EBMs for implicit generation, allowing models to learn data distributions without explicit density functions. This approach leverages energy functions to define the data manifold, enabling flexible generation and manipulation. The code supports both unconditional and conditional generation, allowing for control over the generated data based on specific attributes or classes.

Quick Start & Requirements

• Install prerequisites: pip install -r requirements.txt
• Download pretrained models and unzip into sandbox_cachedir.
• Datasets: MNIST and CIFAR-10 are downloaded automatically. ImageNet and dSprites require manual download via provided wget commands.
• Training examples and demo scripts are available for various datasets and tasks.
• Supports Horovod for distributed training.

Highlighted Details

• Codebase for implicit generation and generalization with EBMs.
• Supports CIFAR-10, ImageNet (32x32 and 128x128), and dSprites datasets.
• Includes scripts for unconditional and conditional training, sampling, and generalization testing.
• Demonstrates concept combination for dSprites dataset.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels (e.g., Discord/Slack) is provided in the README.

Licensing & Compatibility

The README does not explicitly state the license type. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README contains deprecated URLs for downloading ImageNet data. The setup for ImageNet 128x128 requires downloading over 1000 files. No specific versioning or release notes are mentioned, suggesting potential for breaking changes.