132 Repositories
Python anaconda-distribution Libraries
The Pytorch code of "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification", CVPR 2022 (Oral).
DeepBDC for few-shot learning Introduction In this repo, we provide the implementation of the following paper: "Joint Distribution Matters: Dee
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)
MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me
DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.
ChemRxiv | [Paper] XXX DeepStruc Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and the
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)
ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
RODD Official Implementation of 2022 CVPRW Paper RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection Introduction: Recent studie
ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.
VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built
Research using python - Guide for development of research code (using Anaconda Python)
Guide for development of research code (using Anaconda Python) TL;DR: One time s
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt
Out of Distribution Detection on Natural Adversarial Examples
OOD-on-NAE Research project on out of distribution detection for the Computer Vision course by Prof. Rob Fergus (CSCI-GA 2271) Paper out on arXiv - ht
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)
CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper
On Out-of-distribution Detection with Energy-based Models
On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr
Adversarial vulnerability of powerful near out-of-distribution detection
Adversarial vulnerability of powerful near out-of-distribution detection by Stanislav Fort In this repository we're collecting replications for the ke
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
Boltzmann visualization - Visualize the Boltzmann distribution for simple quantum models of molecular motion
Amazon web scraping using Scrapy Framework
Amazon-web-scraping-using-Scrapy-Framework Scrapy Scrapy is an application framework for crawling web sites and extracting structured data which can b
Code to replicate the key results from Exploring the Limits of Out-of-Distribution Detection
Exploring the Limits of Out-of-Distribution Detection In this repository we're collecting replications for the key experiments in the Exploring the Li
Gender Classification Machine Learning Model using Sk-learn in Python with 97%+ accuracy and deployment
Gender-classification This is a ML model to classify Male and Females using some physical characterstics Data. Python Libraries like Pandas,Numpy and
This is a code repository for paper OODformer: Out-Of-Distribution Detection Transformer
OODformer: Out-Of-Distribution Detection Transformer This repo is the official the implementation of the OODformer: Out-Of-Distribution Detection Tran
Language Used: Python . Made in Jupyter(Anaconda) notebook.
FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco
Token drop template on Tezos blockchain, based on Merkle Tree Distribution mechanism.
🛬 Token Drop Template This is a template to perform token drops efficiently on Tezos blockchain. The drop is handled using Merkle Tree Distribution m
The Qis|krypt⟩ is a software suite of protocols of quantum cryptography and quantum communications
The Qis|krypt⟩ is a software suite of protocols of quantum cryptography and quantum communications, as well, other protocols and algorithms, built using IBM’s open-source Software Development Kit for quantum computing Qiskit. ⚛️ 🔐
This is the code of paper ``Contrastive Coding for Active Learning under Class Distribution Mismatch'' with python.
Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame
The official implementation of paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks" (IJCV under review).
DGMS This is the code of the paper "Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks". Installation Our code works with Pytho
Self-supervised learning optimally robust representations for domain generalization.
OptDom: Learning Optimal Representations for Domain Generalization This repository contains the official implementation for Optimal Representations fo
“Robust Lightweight Facial Expression Recognition Network with Label Distribution Training”, AAAI 2021.
EfficientFace Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI
A python module for configuration of block devices
Blivet is a python module for system storage configuration. CI status Licence See COPYING Installation From Fedora repositories Blivet is available in
Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation
OoD_Gen-Chest_Xray Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation Requirements (Installations) Install the following libra
Generates images with semantic content from distribution A in the style of distribution B
A2B Generates images with semantic content from distribution A in the style of d
A software manager for easy development and distribution of Python code
Piper A software manager for easy development and distribution of Python code. The main features that Piper adds to Python are: Support for large-scal
Cilantropy: a Python Package Manager interface created to provide an "easy-to-use" visual and also a command-line interface for Pythonistas.
Cilantropy Cilantropy is a Python Package Manager interface created to provide an "easy-to-use" visual and also a command-line interface for Pythonist
Python package for the analysis and visualisation of finite-difference fields.
discretisedfield Marijan Beg1,2, Martin Lang2, Samuel Holt3, Ryan A. Pepper4, Hans Fangohr2,5,6 1 Department of Earth Science and Engineering, Imperia
[AAAI 2021] EMLight: Lighting Estimation via Spherical Distribution Approximation and [ICCV 2021] Sparse Needlets for Lighting Estimation with Spherical Transport Loss
EMLight: Lighting Estimation via Spherical Distribution Approximation (AAAI 2021) Update 12/2021: We release our Virtual Object Relighting (VOR) Datas
Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.
WOOD Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection. Abstract The training and test data for deep-neural-ne
Official Implementation for the paper DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification
DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification Official Implementation for the pape
A library which implements low-level functions that relate to packaging and distribution of Python
What is it? Distlib is a library which implements low-level functions that relate to packaging and distribution of Python software. It is intended to
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma This is the offi
[ICLR 2021 Spotlight] Pytorch implementation for "Long-tailed Recognition by Routing Diverse Distribution-Aware Experts."
RIDE: Long-tailed Recognition by Routing Diverse Distribution-Aware Experts. by Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu and Stella X. Yu at UC
This repository contains code for the paper "Disentangling Label Distribution for Long-tailed Visual Recognition", published at CVPR' 2021
Disentangling Label Distribution for Long-tailed Visual Recognition (CVPR 2021) Arxiv link Blog post This codebase is built on Causal Norm. Install co
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks
SalFBNet This repository includes Pytorch implementation for the following paper: SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolu
Object Detection with YOLOv3
Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b
This simple script generates a backup of a given Python and R environment
Python Environment Backup It’s always good to maintain your Python and R Anaconda environment packages properly listed and well-kept in case you have
Code for "Long-tailed Distribution Adaptation"
Long-tailed Distribution Adaptation (Accepted in ACM MM2021) This project is built upon BBN. Installation pip install -r requirements.txt Usage Traini
A simple application that calculates the probability distribution of a normal distribution
probability-density-function General info An application that calculates the probability density and cumulative distribution of a normal distribution
OOD Generalization and Detection (ACL 2020)
Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark
FOSS Digital Asset Distribution Platform built on Frappe.
Digistore FOSS Digital Assets Marketplace. Distribute digital assets, like a pro. Video Demo Here Features Create, attach and list digital assets (PDF
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning
Realistic evaluation of transductive few-shot learning Introduction This repo contains the code for our NeurIPS 2021 submitted paper "Realistic evalua
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"
TAUFE: Task-Agnostic Undesirable Feature DeactivationUsing Out-of-Distribution Data
A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power. However, its prediction can be easily biased to undesirable features, which are not essential for solving the target task and are even imperceptible to a human, thereby resulting in poor generalization
WinPython is a portable distribution of the Python programming language for Windows
WinPython tools Copyright © 2012-2013 Pierre Raybaut Copyright © 2014-2019+ The Winpython development team https://github.com/winpython/ Licensed unde
ReAct: Out-of-distribution Detection With Rectified Activations
ReAct: Out-of-distribution Detection With Rectified Activations This is the source code for paper ReAct: Out-of-distribution Detection With Rectified
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
Exact algorithm for computing two-sided statistical tolerance intervals under a normal distribution assumption using Python.
norm-tol-int Exact algorithm for computing two-sided statistical tolerance intervals under a normal distribution assumption using Python. Methods The
Auto locust load test config and worker distribution with Docker and GitHub Action
Auto locust load test config and worker distribution with Docker and GitHub Action Install Fork the repo and change the visibility option to private S
Auto-Encoding Score Distribution Regression for Action Quality Assessment
DAE-AQA It is an open source program reference to paper Auto-Encoding Score Distribution Regression for Action Quality Assessment. 1.Introduction DAE
Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)
Learning Causal Semantic Representation for Out-of-Distribution Prediction This repository is the official implementation of "Learning Causal Semantic
A Python package for generating concise, high-quality summaries of a probability distribution
GoodPoints A Python package for generating concise, high-quality summaries of a probability distribution GoodPoints is a collection of tools for compr
SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning
SASE : Self-Adaptive noise distribution network for Speech Enhancement with heterogeneous data of Cross-Silo Federated learning We propose a SASE mode
Algorithms for calibrating power grid distribution system models
Distribution System Model Calibration Algorithms The code in this library was developed by Sandia National Laboratories under funding provided by the
PyScaffold is a project generator for bootstrapping high quality Python packages
PyScaffold is a project generator for bootstrapping high quality Python packages, ready to be shared on PyPI and installable via pip. It is easy to use and encourages the adoption of the best tools and practices of the Python ecosystem, helping you and your team to stay sane, happy and productive. The best part? It is stable and has been used by thousands of developers for over half a decade!
State-to-Distribution (STD) Model
State-to-Distribution (STD) Model In this repository we provide exemplary code on how to construct and evaluate a state-to-distribution (STD) model fo
An implementation of a discriminant function over a normal distribution to help classify datasets.
CS4044D Machine Learning Assignment 1 By Dev Sony, B180297CS The question, report and source code can be found here. Github Repo Solution 1 Based on t
Code for our paper "MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction" published at ICCV 2021.
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction This repository contains the code for the p
The official PyTorch code implementation of "Personalized Trajectory Prediction via Distribution Discrimination" in ICCV 2021.
Personalized Trajectory Prediction via Distribution Discrimination (DisDis) The official PyTorch code implementation of "Personalized Trajectory Predi
Fully automated download and parsing for Texas A&M University's Registrar's grade distribution PDFs for years 2014+.
Fully automated download and parsing for Texas A&M University's Registrar's grade distribution PDFs for years 2014+. Adds the parsing results to a mySQL database.
Install .deb packages on any distribution:)
Install .deb packages on any distribution:) Install Dependencies The project needs dependencies Python python is often installed by default on linux d
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.
META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu
Normalizing Flows with a resampled base distribution
Resampling Base Distributions of Normalizing Flows Normalizing flows are a popular class of models for approximating probability distributions. Howeve
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN)
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks (MAPDN) This is the implementation of the paper Multi-Age
OoD Minimum Anomaly Score GAN - Code for the Paper 'OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary'
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C
A simple but powerful Python packer to run any project with any virtualenv dependencies anywhwere.
PyEmpaq A simple but powerful Python packer to run any project with any virtualenv dependencies anywhwere. With PyEmpaq you can convert any Python pro
AugMax: Adversarial Composition of Random Augmentations for Robust Training
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an
Arbitrary Distribution Modeling with Censorship in Real Time 59 2 60 3 Bidding Advertising for KDD'21
Arbitrary_Distribution_Modeling This repo implements the Neighborhood Likelihood Loss (NLL) and Arbitrary Distribution Modeling (ADM, with Interacting
The Official Repository for "Generalized OOD Detection: A Survey"
Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec
CloudFormation template and CDK stack that contains a CustomResource with Lambda function to allow the setting of the targetAccountIds attribute of the EC2 Image Builder AMI distribution settings which is not currently supported (as of October 2021) in CloudFormation or CDK.
ec2-imagebuilder-ami-share CloudFormation template and CDK stack that contains a CustomResource with Lambda function to allow the setting of the targe
The Official Repository for "Generalized OOD Detection: A Survey"
Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec
Official codes: Self-Supervised Learning by Estimating Twin Class Distribution
TWIST: Self-Supervised Learning by Estimating Twin Class Distributions Codes and pretrained models for TWIST: @article{wang2021self, title={Self-Sup
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)
Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (
Out-of-distribution detection using the pNML regret. NeurIPS2021
OOD Detection Load conda environment conda env create -f environment.yml or install requirements: while read requirement; do conda install --yes $requ
Exploring the link between uncertainty estimates obtained via "exact" Bayesian inference and out-of-distribution (OOD) detection.
Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)
Python example making use of best practice file structure and multithreading.
Python example making use of best practice file structure and multithreading.
TensorFlow implementation of "Variational Inference with Normalizing Flows"
[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co
distfit - Probability density fitting
Python package for probability density function fitting of univariate distributions of non-censored data
Learning Confidence for Out-of-Distribution Detection in Neural Networks
Learning Confidence Estimates for Neural Networks This repository contains the code for the paper Learning Confidence for Out-of-Distribution Detectio
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)
Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples This project is for the paper "Training Confidence-Calibrated Clas
PowerGym is a Gym-like environment for Volt-Var control in power distribution systems.
Overview PowerGym is a Gym-like environment for Volt-Var control in power distribution systems. The Volt-Var control targets minimizing voltage violat
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021
AAGCN-ACSA EMNLP 2021 Introduction This repository was used in our paper: Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment An
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"
ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo
Official repository for CVPR21 paper "Deep Stable Learning for Out-Of-Distribution Generalization".
StableNet StableNet is a deep stable learning method for out-of-distribution generalization. This is the official repo for CVPR21 paper "Deep Stable L
[ICCV 2021] Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Amplitude-Phase Recombination (ICCV'21) Official PyTorch implementation of "Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neur
Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.
Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets
Official PyTorch implementation of the Fishr regularization for out-of-distribution generalization
Fishr: Invariant Gradient Variances for Out-of-distribution Generalization Official PyTorch implementation of the Fishr regularization for out-of-dist
Python Program that downloads gaming required packages based on your Linux Distribution.
LibreGaming Python Program that downloads gaming required packages based on your Linux Distribution. Table of contents Distributions Prerequisites Dep
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.
SCOOD-UDG (ICCV 2021) This repository is the official implementation of the paper: Semantically Coherent Out-of-Distribution Detection Jingkang Yang,
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)
CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)
CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm
Principled Detection of Out-of-Distribution Examples in Neural Networks
ODIN: Out-of-Distribution Detector for Neural Networks This is a PyTorch implementation for detecting out-of-distribution examples in neural networks.