INORig Source code for CVPR 2021 paper “Riggable 3D Face Reconstruction via In-Network Optimization”. Installation (1) Create an Anaconda environment. conda env create -f env.yaml conda activate INORig (2) Clone the repository and install dependencies. git clone https://github.com/zqbai-jeremy/INORig.git cd INORig pip install -r requirements_pip.txt (3) Setup 3DMM mkdir external cd external git clone https://github.com/zqbai-jeremy/face3d.git cd face3d Setup face3d as in YadiraF/face3d. Download “Exp_Pca.bin” from Guo et al. (in “CoarseData” link of their repository)and copy to “/external/face3d/examples/Data/BFM/Out/”. Download “std_exp.txt” from Deng […]

Scalable Incomplete Network Embedding A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms […]

SGCN A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today’s data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). They have been shown to provide a significant improvement on a wide range of tasks in network analysis, one of which […]

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation contains a lot of my guesses, so I think there are many differences to the official implementations Usage First create lmdb datasets: python prepare_data.py –out LMDB_PATH –n_worker N_WORKER –size SIZE1,SIZE2,SIZE3,… DATASET_PATH This will convert images to jpeg and pre-resizes it. This implementation does not use progressive growing, but you can create multiple resolution datasets using size arguments with comma separated lists, for the cases that you want to try […]

DSANet This project is the PyTorch implementation of the paper “DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting“, in which we propose a dual self-attention network (DSANet) for multivariate time series forecasting. The network architecture is illustrated in the following figure, and more details about the effect of each component can be found in the paper. Requirements Python 3.5 or above PyTorch 1.1 or above pytorch-lightning How to run You need to prepare the dataset first. Check here. # […]

MTTS-CAN Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement. Paper Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff, “Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement”, NeurIPS 2020, Oral Presentation (105 out of 9454 submissions)Link: https://papers.nips.cc/paper/2020/file/e1228be46de6a0234ac22ded31417bc7-Paper.pdf Abstract Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices. These tools can help reduce the risk of exposing patients and […]

Eclipse zenoh Python API Eclipse zenoh is an extremely efficient and fault-tolerant Named Data Networking (NDN) protocol that is able to scale down to extremely constrainded devices and networks. How to install it The Eclipse zenoh-python library is available on Pypi.org.Install the latest available version using pip: pip install eclipse-zenoh WARNING zenoh-python is developped in Rust. On Pypi.org we provide binary wheels for the most common platforms (MacOS, Linux x86). But also a source distribution package for other […]

Network-Shredder Network-Shredder is a python based NIDS. Installation git clone https://github.com/v0lck3r/Network-Shredder.git cd Network-Shredder pip3 install -r requirements.txt Example usage python3 Network-Shredder.py -h Live mode python3 Network-Shredder.py rules.txt –interface tun0 Start web application python3 Network-Shredder.py rules.txt –interface tun0 –web Console quiet mode python3 Network-Shredder.py rules.txt –interface tun0 –web –quiet Specify log directory python3 Network-Shredder.py rules.txt –logdir /tmp/ –interface tun0 –web Pcap based detection python3 Network-Shredder.py rules.txt –pcap pcap_file.pcap GitHub https://github.com/v0lck3r/Network-Shredder    

FisherPruning Group Fisher Pruning for Practical Network Compression(ICML2021) NOTES All models about detection has been released. The classification models will be released later, because we want to refactor all our code into a Hook , so that it can become a more general tool for all tasks in OpenMMLab. We will continue to improve this method and apply it to more other tasks, such as segmentation and pose. The layer grouping algorithm is implemtated based on the AutoGrad of Pytorch, […]

SANet A Slice-Aware Network for Pulmonary Nodule Detection. This paper (SANet) has been accepted and early accessed in IEEE TPAMI 2021. This code and our data are licensed for non-commerical research purpose only. Introduction Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodules. However, it is hard even […]