R3GAN-ImgNet-32x32

Model Card for ImageNet 32x32 R3GAN Model

This model card provides details about the R3GAN model trained on the ImageNet dataset found in the NeurIPS 2024 paper: https://arxiv.org/abs/2501.05441

Model Details

The model achieves 1.27 Frechet Inception Distance-50k on ImageNet64x64 class conditional ImgNet generation.

Model Description

This model is a generative adversarial network (GAN) based on the R3GAN architecture, specifically trained to synthesize high-quality and realistic images from the ImageNet dataset.

• Developed by: Brown University and Cornell University
• Funded by: National Science Foundation and National Institute of Health (See paper for funding details)
• Shared by: [Optional: Specify sharer if different from developer]
• Model type: Generative Adversarial Network
• Language(s) (NLP): N/A
• License: [Specify License, e.g., MIT, Apache 2.0, or a custom license]
• Finetuned from model: N/A

Model Sources

• Repository: https://github.com/brownvc/R3GAN/
• Paper: https://openreview.net/forum?id=OrtN9hPP7V
• Demo: [Optional: Provide a link to a demo or example usage]

Uses

Direct Use

This model can be used to generate high-resolution images similar to those in the ImageNet dataset. Its primary application includes research in generative models and image synthesis.

Downstream Use

The model can be fine-tuned for specific subsets of the ImageNet dataset or other similar datasets for domain-specific image generation tasks.

Out-of-Scope Use

The model should not be used for generating deceptive or misleading content, malicious purposes, or tasks where realistic image synthesis could cause harm.

Bias, Risks, and Limitations

The model inherits biases present in the ImageNet dataset, including potential overrepresentation or underrepresentation of certain classes. Users should critically evaluate and mitigate biases before deploying the model.

Recommendations

• Avoid using the model for sensitive applications without thorough bias evaluation.
• Ensure appropriate credit is given when publishing or sharing generated images.

How to Get Started with the Model

Below is an example of how to use the model for image generation:

• Will add later