Generative Topological Networks
<|reference_start|>Generative Topological Networks: Generative models have seen significant advancements in recent years, yet often remain challenging and costly to train and use. We introduce Generative Topological Networks (GTNs) -- a new class of generative models that addresses these shortcomings. GTNs are trained deterministically using a simple supervised learning approach grounded in topology theory. GTNs are fast to train, and require only a single forward pass in a standard feedforward neural network to generate samples. We demonstrate the strengths of GTNs on several datasets, including MNIST, CelebA and the Hands and Palm Images dataset. Finally, the theory behind GTNs offers insights into how to train generative models for improved performance. Code and weights are available at: https://github.com/alonalj/GTN<|reference_end|>
@article{levy-jurgenson2024generative,
    title={Generative Topological Networks},
    author={Alona Levy-Jurgenson, Zohar Yakhini},
    journal={arXiv preprint arXiv:2406.15152},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15152},
    primaryClass={cs.LG cs.AI stat.ML}
}