Python Convolutional-networks Resources

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.

HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati

Z-PNN: Zoom Pansharpening Neural Network Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for

LearningPsychologicalSpaces v0.1: v1.1: v1.2: v1.3: v1.4: v1.5: The code in this repository explores learning a mapping from images to psychological s

Salad Salad is an Open Source Python library to segment tissues into different biologically relevant regions based on Hidden Markov Random Fields. The

time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

ROCKET + MINIROCKET ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge D

DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc

iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

ConvCRF This repository contains the reference implementation for our proposed Convolutional CRFs in PyTorch (Tensorflow planned). The two main entry-

High-resolution networks and Segmentation Transformer for Semantic Segmentation Branches This is the implementation for HRNet + OCR. The PyTroch 1.1 v

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation This repository contains the source code of our paper, ESPNet (acc

Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation This implementation is based on orobix implement

Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor

TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under

Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Segmentation-Aware Convolutional Networks Using Local Attention Masks [Project Page] [Paper] Segmentation-aware convolution filters are invariant to b

Full-Resolution Residual Networks (FRRN) This repository contains code to train and qualitatively evaluate Full-Resolution Residual Networks (FRRNs) a

PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+

Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation: Work In Progress, Results can't be replicated yet with the m

FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th

Introduction This repo contains the code to train and evaluate FC-DenseNets as described in The One Hundred Layers Tiramisu: Fully Convolutional Dense

keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git

Keras-FCN This repository contains my implementation of Fully Convolutional Networks in Keras (Tensorflow backend). Currently, semantic segmentation c

Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the

FCN-semantic-segmentation Simple end-to-end semantic segmentation using fully convolutional networks [1]. Takes a pretrained 34-layer ResNet [2], remo

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w

FCN.tensorflow Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (FCNs). The implementation is largely based on the

FCN_via_Keras FCN FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network

MatConvNet implementation of the FCN models for semantic segmentation This package contains an implementation of the FCN models (training and evaluati

SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Caffe SegNet This is a modified version of Caffe which supports the SegNet architecture As described in SegNet: A Deep Convolutional Encoder-Decoder A

Retina blood vessel segmentation with a convolution neural network (U-net) This repository contains the implementation of a convolutional neural netwo

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

ZF_UNET_224 Pretrained Model Modification of convolutional neural net "UNET" for image segmentation in Keras framework Requirements Python 3.*, Keras

GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is

AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

GOAT A paper using optimal transport to solve the graph matching problem. https://arxiv.org/abs/2111.05366 Repo structure .github: Files specifying ho

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

FPS-Net Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation", accepted by ISPRS journal of Photogrammetry

Pointer networks Tensorflow2 原文：https://arxiv.org/abs/1506.03134 仅供参考与学习，内含代码备注 环境 tensorflow==2.6.0 tqdm matplotlib numpy 《pointer networks》阅读笔记 应用场景

Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C

EquivariantSelfAttention An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astro

Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

InterpretableMDE A PyTorch implementation for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper. arXiv link: https://arxiv.or

Conditional Variational Capsule Network for Open Set Recognition This repository hosts the official code related to "Conditional Variational Capsule N

Dynamic Data Augmentation with Gating Networks This is an official PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

DEDMAP is a Simple but Powerful, Clever and Flexible Cross-Platform Port Scanning tool made with ease to use and convenience in mind. Both TCP

RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network An official PyTorch implementation of the RBSRICNN network as desc

Package Description The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. snlstm is developed to provide

snn-localization repo PyTorch implementation of Spiking Neural Networks trained on surrogate gradient & BPTT using snntorch. Install Dependencies Orig

This repository contains a number of convolutional neural network visualization techniques implemented in PyTorch.

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks Code for “Efficient Sharpness-aware Minimization for Improved Training

Project Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs, https://arxiv.org/pdf/2111.01940.pdf. Authors Truong Son Hy

Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

CMKN Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021. Testing Yo

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

☢️ Audiomer ☢️ Audiomer: A Convolutional Transformer for Keyword Spotting [ arXiv ] [ Previous SOTA ] [ Model Architecture ] Results on SpeechCommands

DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning

FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.

Can we learn gradients by Hamiltonian Neural Networks? This project was carried out as part of the Optimization for Machine Learning course (CS-439) a

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems This repository is the official implementation of Rever

MedAI: Transparency in Medical Image Segmentation What is this repo This repo contains the code and experiments that are implemented to contribute in

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (Local-Lip) Introduction TL;DR: We propose an efficient and trainabl

Generative Adversarial Networks(GANs) Vanilla GAN ClusterGAN Vanilla GAN Model Structure Final Generator Structure A MLP with 2 hidden layers of hidde

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng

A large-scale database for graph representation learning

UltraGCN This is our Pytorch implementation for our CIKM 2021 paper: Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. UltraGCN: A

Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks This repository contains the code and pre-trained

Wide-Networks This repository contains the code of various experiments on wide neural networks. In particular, we implement classes for abc-parameteri

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Abstract Graph Neural Networks (GNNs) are widely used on a

OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati

Face-Detection-with-MTCNN Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to sol

Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age

The Network Simulator, Version 3 Table of Contents: An overview Building ns-3 Running ns-3 Getting access to the ns-3 documentation Working with the d