[NeurIPS 2022] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
-
Updated
Mar 18, 2024 - Python
[NeurIPS 2022] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
Generative models that generate paintings in the style of Bob Ross based on segmentation images.
[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs
This Repository Contains Solution to the Assignments of the Generative Adversarial Networks (GANs) Specialization from deeplearning.ai on Coursera Taught by Sharon Zhou, Eda Zhou, Eric Zelikman
Anime face generation: from simple GAN to GauGAN Conditional Generation
A new interactive digital home for an extinct species.
[CVPR 2021] Teachers Do More Than Teach: Compressing Image-to-Image Models (CAT)
Portrait Drawing Generation, ICME 2021, BEST DEMO RUNNER UP AWARD
Experimentación con Redes Generativas Adversarias en busca de un modelo que genere carreteras realistas multimodales.
Generative adversarial network is used to train neural network model to create real image from drawing. Pix2pix tensor flow code is refereed and paint tool is created to interpret trained model results
Add a description, image, and links to the gaugan topic page so that developers can more easily learn about it.
To associate your repository with the gaugan topic, visit your repo's landing page and select "manage topics."