A collection of awesome video generation studies.
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Updated
May 9, 2024 - TeX
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
A collection of awesome video generation studies.
A Bachelor Degree Project using GAN to Generate Synthetic Aperture Radar Images
Implementation of VideoGigaGAN, SOTA video upsampling out of Adobe AI labs, in Pytorch
A collection of awesome image inpainting studies.
[IEEE TIP] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
StyleGAN in PyTorch
TensorFlow implementation of ProbCast model for probabilistic time series forecasting with generative adversarial networks.
TensorFlow implementation of TimeGAN model for synthetic time series generation with generative adversarial networks.
PyTorch implementation of 'PGGAN' (Karras et al., 2018) from scratch and training it on CelebA-HQ at 512 × 512
The official code implementation of "Towards Interactive Image Inpainting via Sketch Refinement".
Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data (WACV 2023)
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
PyTorch implementation of 'CycleGAN' (Zhu et al., 2017) and training it on 6 datasets
TensorFlow implementation of MRIC (Multi-Realism Image Compression with a Conditional Generator, CVPR 2023)
Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754
Deep Learning in Haskell
Estimating brain activity for a stimulus as measured by fMRI using a volumetric conditional Generative Adversarial Network (GAN) model.
A goal-oriented X-ray restoration approach with Restore-to-Classify GANs.
DD2402 Advanced Individual Course in Computational Biology Project
Comparisons of Drug Generation Models
Released June 10, 2014