364 Repositories
Python unsupervised-denoising Libraries
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"
The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami · Rayhane Mama · Ragavan Thurairatn
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Bilateral Denoising Diffusion Models (BDDMs) This is the official PyTorch implementation of the following paper: BDDM: BILATERAL DENOISING DIFFUSION M
Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022)
Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022) Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, and Guang Chen. Uns
[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior
GP-UNIT - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Unsupervised Image-to-
Comprehensive-E2E-TTS - PyTorch Implementation
A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS
FreeSOLO for unsupervised instance segmentation, CVPR 2022
FreeSOLO: Learning to Segment Objects without Annotations This project hosts the code for implementing the FreeSOLO algorithm for unsupervised instanc
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn
Official code for :rocket: Unsupervised Change Detection of Extreme Events Using ML On-Board :rocket:
RaVAEn The RaVÆn system We introduce the RaVÆn system, a lightweight, unsupervised approach for change detection in satellite data based on Variationa
The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.
Generative Modeling with Optimal Transport Maps The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal
TigerLily: Finding drug interactions in silico with the Graph.
Drug Interaction Prediction with Tigerlily Documentation | Example Notebook | Youtube Video | Project Report Tigerlily is a TigerGraph based system de
Language Models Can See: Plugging Visual Controls in Text Generation
Language Models Can See: Plugging Visual Controls in Text Generation Authors: Yixuan Su, Tian Lan, Yahui Liu, Fangyu Liu, Dani Yogatama, Yan Wang, Lin
Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an
Unsupervised phone and word segmentation using dynamic programming on self-supervised VQ features.
Unsupervised Phone and Word Segmentation using Vector-Quantized Neural Networks Overview Unsupervised phone and word segmentation on speech data is pe
(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"
(CVPR 2022) TokenCut Pytorch implementation of Tokencut: Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut Yangtao W
Get started with Machine Learning with Python - An introduction with Python programming examples
Machine Learning With Python Get started with Machine Learning with Python An engaging introduction to Machine Learning with Python TL;DR Download all
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology (LMRL Workshop, NeurIPS 2021)
Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology Self-Supervised Vision Transformers Learn Visual Concepts in Histopatholog
Official Implementation for the "An Empirical Investigation of 3D Anomaly Detection and Segmentation" paper.
An Empirical Investigation of 3D Anomaly Detection and Segmentation Project | Paper Official PyTorch Implementation for the "An Empirical Investigatio
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch
Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P
Code for "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" paper
UNICORN 🦄 Webpage | Paper | BibTex PyTorch implementation of "Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency" pap
Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation by Lukas Hoyer, Dengxin Dai, and Luc Van Gool [Arxiv] [Paper] Overview Unsup
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling
CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce
scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics.
scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA
PyTorch Implementation of "Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging" (Findings of ACL 2022)
Feature_CRF_AE Feature_CRF_AE provides a implementation of Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the
This is an official implementation of the CVPR2022 paper "Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots".
Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots Blind2Unblind Citing Blind2Unblind @inproceedings{wang2022blind2unblind, tit
PyTorch Implementation of DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
DiffGAN-TTS - PyTorch Implementation PyTorch implementation of DiffGAN-TTS: High
Large-Scale Unsupervised Object Discovery
Large-Scale Unsupervised Object Discovery Huy V. Vo, Elena Sizikova, Cordelia Schmid, Patrick Pérez, Jean Ponce [PDF] We propose a novel ranking-based
CATE: Computation-aware Neural Architecture Encoding with Transformers
CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans
Implementation of SOMs (Self-Organizing Maps) with neighborhood-based map topologies.
py-self-organizing-maps Simple implementation of self-organizing maps (SOMs) A SOM is an unsupervised method for learning a mapping from a discrete ne
CLASSIX is a fast and explainable clustering algorithm based on sorting
CLASSIX Fast and explainable clustering based on sorting CLASSIX is a fast and explainable clustering algorithm based on sorting. Here are a few highl
Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies
py-self-organizing-map Simple implementation of Self Organizing Maps (SOMs) with rectangular and hexagonal grid topologies. A SOM is a simple unsuperv
DimReductionClustering - Dimensionality Reduction + Clustering + Unsupervised Score Metrics
Dimensionality Reduction + Clustering + Unsupervised Score Metrics Introduction
The 7th edition of NTIRE: New Trends in Image Restoration and Enhancement workshop will be held on June 2022 in conjunction with CVPR 2022.
NTIRE 2022 - Image Inpainting Challenge Important dates 2022.02.01: Release of train data (input and output images) and validation data (only input) 2
traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.
traiNNer traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation to
Lipschitz-constrained Unsupervised Skill Discovery
Lipschitz-constrained Unsupervised Skill Discovery This repository is the official implementation of Seohong Park, Jongwook Choi*, Jaekyeom Kim*, Hong
Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set
Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set This is the repository for the Deep Learning proje
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
SNCSE SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples This is the repository for SNCSE. SNCSE aims to allev
PyTorch implementation of "VRT: A Video Restoration Transformer"
VRT: A Video Restoration Transformer Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc Van Gool Computer
Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses
Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution This code belongs to the paper [1] available at https://arx
This is the source code for the experiments related to the paper Unsupervised Audio Source Separation Using Differentiable Parametric Source Models
Unsupervised Audio Source Separation Using Differentiable Parametric Source Models This is the source code for the experiments related to the paper Un
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning
Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction Official github repository for the paper High Fidelity De
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning This repository contains the code and relevant instructions
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms
A library for benchmarking, developing and deploying deep learning anomaly detection algorithms Key Features • Getting Started • Docs • License Introd
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Official TensorFlow implementation of the unsupervised reconstruction model using zero-Shot Learned Adversarial TransformERs (SLATER). (https://arxiv.
DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency
[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper) Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang PDF:
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"
PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi
The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach
The trained model and denoising example for paper : Cardiopulmonary Auscultation Enhancement with a Two-Stage Noise Cancellation Approach
Unsupervised text tokenizer focused on computational efficiency
YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)
Denoising images with Fourier Ring Correlation loss
Denoising images with Fourier Ring Correlation loss The python code accompanies the working manuscript Image quality measurements and denoising using
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”
HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The
Labels4Free: Unsupervised Segmentation using StyleGAN
Labels4Free: Unsupervised Segmentation using StyleGAN ICCV 2021 Figure: Some segmentation masks predicted by Labels4Free Framework on real and synthet
Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"
LQVAE-separation Code for "Unsupervised Source Separation via Bayesian inference in the latent domain" Paper Samples GT Compressed Separated Drums GT
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.
Lbl2Vec Lbl2Vec is an algorithm for unsupervised document classification and unsupervised document retrieval. It automatically generates jointly embed
A High-Quality Real Time Upscaler for Anime Video
Anime4K Anime4K is a set of open-source, high-quality real-time anime upscaling/denoising algorithms that can be implemented in any programming langua
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising (CVPR 2020 Oral & TPAMI 2021)
ELD The implementation of CVPR 2020 (Oral) paper "A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising" and its journal (TPAMI) v
Algorithms for outlier, adversarial and drift detection
Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline d
(JMLR' 19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Python Outlier Detection (PyOD) Deployment & Documentation & Stats & License PyOD is a comprehensive and scalable Python toolkit for detecting outlyin
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)
U-GAT-IT — Official TensorFlow Implementation (ICLR 2020) : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization fo
Anomaly detection related books, papers, videos, and toolboxes
Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data This is the official PyTorch implementation of the SeCo paper: @articl
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.
Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal
Histology images query (unsupervised)
110-1-NTU-DBME5028-Histology-images-query Final Project: Histology images query (unsupervised) Kaggle: https://www.kaggle.com/c/histology-images-query
Machine learning library for fast and efficient Gaussian mixture models
This repository contains code which implements the Stochastic Gaussian Mixture Model (S-GMM) for event-based datasets Dependencies CMake Premake4 Blaz
(EI 2022) Controllable Confidence-Based Image Denoising
Image Denoising with Control over Deep Network Hallucination Paper and arXiv preprint -- Our frequency-domain insights derive from SFM and the concept
A simple Tensorflow based library for deep and/or denoising AutoEncoder.
libsdae - deep-Autoencoder & denoising autoencoder A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn st
Rethinking the Truly Unsupervised Image-to-Image Translation - Official PyTorch Implementation (ICCV 2021)
Rethinking the Truly Unsupervised Image-to-Image Translation (ICCV 2021) Each image is generated with the source image in the left and the average sty
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation AniGAN: Style-Guided Generative Adversarial Networks for U
IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation
IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation Independent Encoder for Deep
NICE-GAN — Official PyTorch Implementation Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
NICE-GAN-pytorch - Official PyTorch implementation of NICE-GAN: Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning
MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python
UI2I via StyleGAN2 - Unsupervised image-to-image translation method via pre-trained StyleGAN2 network
We proposed an unsupervised image-to-image translation method via pre-trained StyleGAN2 network. paper: Unsupervised Image-to-Image Translation via Pr
ZUNIT - Toward Zero-Shot Unsupervised Image-to-Image Translation
ZUNIT Dependencies you can install all the dependencies by pip install -r requirements.txt Datasets Download CUB dataset. Unzip the birds.zip at ./da
Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
U-GAT-IT — Official PyTorch Implementation : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Imag
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation
Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle
DualGAN-tensorflow: tensorflow implementation of DualGAN
ICCV paper of DualGAN DualGAN: unsupervised dual learning for image-to-image translation please cite the paper, if the codes has been used for your re
Unsupervised captioning - Code for Unsupervised Image Captioning
Unsupervised Image Captioning by Yang Feng, Lin Ma, Wei Liu, and Jiebo Luo Introduction Most image captioning models are trained using paired image-se
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'
PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi
VQMIVC - Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion (Interspeech
Minimal diffusion models - Minimal code and simple experiments to play with Denoising Diffusion Probabilistic Models (DDPMs)
Minimal code and simple experiments to play with Denoising Diffusion Probabilist
The official MegEngine implementation of the ICCV 2021 paper: GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning
[ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning This is the official implementation of our ICCV2021 paper GyroFlow. Our pres
Ladder network is a deep learning algorithm that combines supervised and unsupervised learning
This repository contains source code for the experiments in a paper titled Semi-Supervised Learning with Ladder Networks by A Rasmus, H Valpola, M Hon
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper
An implementation of Deep Graph Infomax (DGI) in PyTorch
DGI Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341 Overview Here we provide an implementation of Deep Graph Infom
Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)
Unsupervised Attributed Multiplex Network Embedding (DMGI) Overview Nodes in a multiplex network are connected by multiple types of relations. However
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
Graph-InfoClust-GIC [PAKDD 2021] PAKDD'21 version Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs Preprint version Graph InfoClu
Official code for "InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization" (ICLR 2020, spotlight)
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization Authors: Fan-yun Sun, Jordan Hoffm
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.
CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we
Autoregressive Predictive Coding: An unsupervised autoregressive model for speech representation learning
Autoregressive Predictive Coding This repository contains the official implementation (in PyTorch) of Autoregressive Predictive Coding (APC) proposed
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut
Unsupervised Representation Learning by Invariance Propagation
Unsupervised Learning by Invariance Propagation This repository is the official implementation of Unsupervised Learning by Invariance Propagation. Pre
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
Learning to Classify Images without Labels This repo contains the Pytorch implementation of our paper: SCAN: Learning to Classify Images without Label
Official PyTorch implementation of the paper "Self-Supervised Relational Reasoning for Representation Learning", NeurIPS 2020 Spotlight.
Official PyTorch implementation of the paper: "Self-Supervised Relational Reasoning for Representation Learning" (2020), Patacchiola, M., and Storkey,
Unsupervised Learning of Video Representations using LSTMs
Unsupervised Learning of Video Representations using LSTMs Code for paper Unsupervised Learning of Video Representations using LSTMs by Nitish Srivast
Code and training data for our ECCV 2016 paper on Unsupervised Learning
Shuffle and Learn (Shuffle Tuple) Created by Ishan Misra Based on the ECCV 2016 Paper - "Shuffle and Learn: Unsupervised Learning using Temporal Order
Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.
Video Representation Learning by Recognizing Temporal Transformations [Project Page] Simon Jenni, Givi Meishvili, and Paolo Favaro. In ECCV, 2020. Thi
Official Pytorch implementation for AAAI2021 paper (RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning)
RSPNet Official Pytorch implementation for AAAI2021 paper "RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning" [Suppleme
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
Realtime Unsupervised Depth Estimation from an Image This is the caffe implementation of our paper "Unsupervised CNN for single view depth estimation:
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
SfMLearner This codebase implements the system described in the paper: Unsupervised Learning of Depth and Ego-Motion from Video Tinghui Zhou, Matthew