1591 Repositories
Python adversarial-networks Libraries
A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations.
IllustrationGAN A simple, clean TensorFlow implementation of Generative Adversarial Networks with a focus on modeling illustrations. Generated Images
Learning Chinese Character style with conditional GAN
zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks Introduction Learning eastern asian language typefaces with GAN. zi2zi(字到字, me
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"
BEGAN in Tensorflow Tensorflow implementation of BEGAN: Boundary Equilibrium Generative Adversarial Networks. Requirements Python 2.7 or 3.x Pillow tq
Code and hyperparameters for the paper "Generative Adversarial Networks"
Generative Adversarial Networks This repository contains the code and hyperparameters for the paper: "Generative Adversarial Networks." Ian J. Goodfel
Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.
Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl
Curated list of awesome GAN applications and demo
gans-awesome-applications Curated list of awesome GAN applications and demonstrations. Note: General GAN papers targeting simple image generation such
A list of all named GANs!
The GAN Zoo Every week, new GAN papers are coming out and it's hard to keep track of them all, not to mention the incredibly creative ways in which re
Collection of generative models in Tensorflow
tensorflow-generative-model-collections Tensorflow implementation of various GANs and VAEs. Related Repositories Pytorch version Pytorch version of th
Build Graph Nets in Tensorflow
Graph Nets library Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact [email protected] for comments a
Hummingbird compiles trained ML models into tensor computation for faster inference.
Hummingbird Introduction Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to se
PyClustering is a Python, C++ data mining library.
pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (C++ pyclustering library) of each algorithm or model. C++ pyclustering library is a part of pyclustering and supported for Linux, Windows and MacOS operating systems.
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom Self Organizing Maps MiniSom is a minimalistic and Numpy based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial N
Computations and statistics on manifolds with geometric structures.
Geomstats Code Continuous Integration Code coverage (numpy) Code coverage (autograd, tensorflow, pytorch) Documentation Community NEWS: Geomstats is r
This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].
CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R
Official implementation for "Image Quality Assessment using Contrastive Learning"
Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi
DeepMReye: magnetic resonance-based eye tracking using deep neural networks
DeepMReye: magnetic resonance-based eye tracking using deep neural networks
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks
SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks
Pytorch implementation of forward and inverse Haar Wavelets 2D
Pytorch implementation of forward and inverse Haar Wavelets 2D
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi
⚖️🔁🔮🕵️♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.
Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co
Generating Band-Limited Adversarial Surfaces Using Neural Networks
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 (NeurIPS*2021)
Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.
Improving the robustness and performance of biomedical NLP models through adversarial training
RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment
Predict halo masses from simulations via graph neural networks
HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati
Pansharpening by convolutional neural networks in the full resolution framework
Z-PNN: Zoom Pansharpening Neural Network Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for
Learning a mapping from images to psychological similarity spaces with neural networks.
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
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
This is a Python implementation of the HMRF algorithm on networks with categorial variables.
Salad Salad is an Open Source Python library to segment tissues into different biologically relevant regions based on Hidden Markov Random Fields. The
Short and long time series classification using convolutional neural networks
time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f
sktime companion package for deep learning based on TensorFlow
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
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc
A toolbox to iNNvestigate neural networks' predictions!
iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
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 is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.
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
High-resolution networks and Segmentation Transformer for Semantic Segmentation
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
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
Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation This implementation is based on orobix implement
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
Attention Gated Networks (Image Classification & Segmentation) Pytorch implementation of attention gates used in U-Net and VGG-16 models. The framewor
Understanding Convolution for Semantic Segmentation
TuSimple-DUC by Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, and Garrison Cottrell. Introduction This repository is for Under
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s
Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)
Pixel Transposed Convolutional Networks Created by Hongyang Gao, Hao Yuan, Zhengyang Wang and Shuiwang Ji at Texas A&M University. Introduction Pixel
Chainer Implementation of Semantic Segmentation using Adversarial Networks
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
Segmentation-Aware Convolutional Networks Using Local Attention Masks [Project Page] [Paper] Segmentation-aware convolution filters are invariant to b
Full Resolution Residual Networks for Semantic Image Segmentation
Full-Resolution Residual Networks (FRRN) This repository contains code to train and qualitatively evaluate Full-Resolution Residual Networks (FRRNs) a
PSPNet in Chainer
PSPNet This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Training Requirement Python 3.4.4+ Chainer 3.0.0b1+
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Multipath RefineNet A MATLAB based framework for semantic image segmentation and general dense prediction tasks on images. This is the source code for
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images
Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K
Fully Convolutional DenseNet (A.K.A 100 layer tiramisu) for semantic segmentation of images implemented in TensorFlow.
FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th
A playable implementation of Fully Convolutional Networks with Keras.
keras-fcn A re-implementation of Fully Convolutional Networks with Keras Installation Dependencies keras tensorflow Install with pip $ pip install git
My implementation of Fully Convolutional Neural Networks in Keras
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 with caffe for the cityscapes dataset
Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the
Fully convolutional networks for semantic segmentation
FCN-semantic-segmentation Simple end-to-end semantic segmentation using fully convolutional networks [1]. Takes a pretrained 34-layer ResNet [2], remo
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)
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
##A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation. #USAGE To run the trained classifier on some images: python w
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation
FCN.tensorflow Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (FCNs). The implementation is largely based on the
Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation(Unfinished)
Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas
An Implementation of Fully Convolutional Networks in Tensorflow.
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 by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.
Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network
A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation
MatConvNet implementation of the FCN models for semantic segmentation This package contains an implementation of the FCN models (training and evaluati
U-Net: Convolutional Networks for Biomedical Image Segmentation
Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne
Code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”
GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is
A graph adversarial learning toolbox based on PyTorch and DGL.
GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat
Models Supported: AlbUNet [18, 34, 50, 101, 152] (1D and 2D versions for Single and Multiclass Segmentation, Feature Extraction with supports for Deep Supervision and Guided Attention)
AlbUNet-1D-2D-Tensorflow-Keras This repository contains 1D and 2D Signal Segmentation Model Builder for AlbUNet and several of its variants developed
Interactive convnet features visualization for Keras
Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv
A paper using optimal transport to solve the graph matching problem.
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
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)Complex-Valued Neural Networks (CVNN)
Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE
SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211
Pointer networks Tensorflow2
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
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.
This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks This is our Pytorch implementation for the paper: Zirui Zhu, Chen Gao, Xu C
Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.
Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu
Code for paper "Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features"
Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features Train python main.py --dataset brazil-flights C
A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021)
GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i
Code repo for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper.
InterpretableMDE A PyTorch implementation for "Towards Interpretable Deep Networks for Monocular Depth Estimation" paper. arXiv link: https://arxiv.or
Code for "Adversarial attack by dropping information." (ICCV 2021)
AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa
code for "Feature Importance-aware Transferable Adversarial Attacks"
Feature Importance-aware Attack(FIA) This repository contains the code for the paper: Feature Importance-aware Transferable Adversarial Attacks (ICCV
Code for the Paper: Conditional Variational Capsule Network for Open Set Recognition
Conditional Variational Capsule Network for Open Set Recognition This repository hosts the official code related to "Conditional Variational Capsule N
Code repository for EMNLP 2021 paper 'Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods'
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods This is the code repository to accompany the EMNLP 2021 paper on ad
PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks
Dynamic Data Augmentation with Gating Networks This is an official PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks
A Simple but Powerful cross-platform port scanning & and network automation tool.
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
LSTM Neural Networks for Spectroscopic Studies of Type Ia Supernovae
Package Description The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. snlstm is developed to provide
PyTorch implementation of Spiking Neural Networks trained on surrogate gradient & BPTT using snntorch.
snn-localization repo PyTorch implementation of Spiking Neural Networks trained on surrogate gradient & BPTT using snntorch. Install Dependencies Orig
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''
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-Routing is a Python simulator for experimenting routing algorithms and mac protocols on unmanned aerial vehicle networks.
UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa
Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper
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
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks Code for “Efficient Sharpness-aware Minimization for Improved Training
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
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
Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi
RMNet: Equivalently Removing Residual Connection from Networks
RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.
This library is testing the ethics of language models by using natural adversarial texts.
prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
DistMIS Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation. DistriMIS Distributing Deep Learning Hyperparameter Tuning
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach
Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu
FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.
FastCover: A Self-Supervised Learning Framework for Multi-Hop Influence Maximization in Social Networks by Anonymous.
Can we learn gradients by Hamiltonian Neural Networks?
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