This repository is dedicated to sharing open-source implementations of fundamental generative models in artificial general intelligence (AGI). The goal is to provide a comprehensive resource for researchers and practitioners interested in exploring and experimenting with these models.
Currently, this repository includes the following generative models:
- Variational Autoencoder (VAE)
- Generative Adversarial Network (GAN)
- Autoregressive models
- Normalizing Flows
- Boltzmann Machines
- Hopfield Networks
- Diffusion Model
Each model has a separate directory containing the implementation code and a brief description of the model.
The implementations are provided in Python using PyTorch. To use these models, clone this repository and install the required dependencies specified in the requirements.txt file. Each model has its own script for training and generating samples. The script can be run using the command python <model_name>_train.py and python <model_name>_generate.py.
Contributions are welcome in the form of new models, bug fixes, or improved implementations. If you wish to contribute, please follow the guidelines provided in the CONTRIBUTING.md file.
This repository is licensed under the MIT License. See the LICENSE file for more details.
The implementations in this repository are based on the following papers:
- Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
- Oord, A. van den, Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759.
- Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2017). Density estimation using real NVP. arXiv preprint arXiv:1605.08803.
- Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation, 14(8), 1771-1800.
- Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.