Popular GitHub Repositories for Stylegan Encoder

?? - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

A neural network that transforms a design mock-up into a static website.

PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.

Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

[CVPR2020] Adversarial Latent Autoencoders

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework

Top2Vec learns jointly embedded topic, document and word vectors.

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Paper Lists for Graph Neural Networks

The implementation of DeBERTa

ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang

High Quality Monocular Depth Estimation via Transfer Learning

[ACL 2024 ?] Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

:robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+