Projects sharing features with Fyu Drn

This project is a JavaScript library designed for real-time face detection directly within a web browser. It functions as a machine learning model wrapper that enables developers to identify and track human faces in live video streams without the need for backend server processing. The library utilizes browser-native media access to stream raw camera data into application memory, where it performs pixel-level analysis. By leveraging a tensor-based inference engine and web-assembly acceleration, the tool executes complex neural network calculations locally to achieve high-performance computer

Code for the ACL 2017 paper "Get To The Point: Summarization with Pointer-Generator Networks"

PyTorch implementation of UNet++ (Nested U-Net).

Shanshan Zhao♯ , Qing-Guo Chen , Xuerui Qiu , Jialun Liu , Zhao Xu , Weihua Luo , Kaifu Zhang , Yanye Lu♯

vrn is a 3D face reconstruction tool that generates three-dimensional volumetric representations of human faces from single two-dimensional images. It utilizes a volumetric convolutional neural network regression model to predict 3D volume data directly from image pixels. The system converts these volumetric predictions into 3D meshes through isosurface extraction and vertex coloring. It further applies realistic surface details by mapping two-dimensional image pixels onto the resulting 3D mesh using nearest-neighbor texture projection. The project provides capabilities for single-image dept

Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks

The author's officially unofficial PyTorch BigGAN implementation.

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

G-SchNet is a generative neural network that samples molecules by sequentially placing atoms in 3d space. It can be trained on data sets of 3d molecules with variable sizes and compositions. The conditional version, cG-SchNet, explicitly takes chemical and structural properties into account to…

A PyTorch implementation of DenseNet.

Fast Neural Style for Image Style Transform by Pytorch

Convolutional Recurrent Neural Network

Keras implementation of Deeplab v3+ with pretrained weights

Tensorflow implementation for reproducing main results in the paper Self-Attention Generative Adversarial Networks by Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.