1213 Repositories
Python self-training-ensembles Libraries
[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
SADRNet Paper link: SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction Requirements python
Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers"
Focal Transformer This is the official implementation of our Focal Transformer -- "Focal Self-attention for Local-Global Interactions in Vision Transf
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21
MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen
We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.
Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D
PyTorch DepthNet Training on Still Box dataset
DepthNet training on Still Box Project page This code can replicate the results of our paper that was published in UAVg-17. If you use this repo in yo
An attendance bot that joins google meet automatically according to schedule and marks present in the google meet.
Google-meet-self-attendance-bot An attendance bot which joins google meet automatically according to schedule and marks present in the google meet. I
PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT.
MoCo v3 for Self-supervised ResNet and ViT Introduction This is a PyTorch implementation of MoCo v3 for self-supervised ResNet and ViT. The original M
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".
The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings and that the spatial embeddings make minor contributions, increasing the need for high-quality content embeddings and thus increasing the training difficulty.
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation (ICCV2021)
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad
A Code that can make your Discord Account 24/7 on Voice Channels!
Voicecord Make your Discord Account Online 24/7 on Voice Channels! A Code written in Python that helps you to keep your account 24/7 on Voice Channels
LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION
Query Selector Here you can find code and data loaders for the paper https://arxiv.org/pdf/2107.08687v1.pdf . Query Selector is a novel approach to sp
Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.
An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin
IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020)
This repo is the official implementation of our paper "Instance Adaptive Self-training for Unsupervised Domain Adaptation". The purpose of this repo is to better communicate with you and respond to your questions. This repo is almost the same with Another-Version, and you can also refer to that version.
Ongoing research training transformer language models at scale, including: BERT & GPT-2
What is this fork of Megatron-LM and Megatron-DeepSpeed This is a detached fork of https://github.com/microsoft/Megatron-DeepSpeed, which in itself is
We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University
This is an official implementation for "Self-Supervised Learning with Swin Transformers".
Self-Supervised Learning with Vision Transformers By Zhenda Xie*, Yutong Lin*, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao and Han Hu This repo is the
(CVPR2021) Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain Mingchen Zhuge*, Dehong Gao*, Deng-Ping Fan#, Linbo Jin, Ben Chen, Haoming Zhou, Minghui
Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.
Updates (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training. Pyr
LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA
LightSeq: A High Performance Library for Sequence Processing and Generation
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".
Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based
By default, networkx has problems with drawing self-loops in graphs.
By default, networkx has problems with drawing self-loops in graphs. It makes it hard to draw a graph with self-loops or to make a nicely looking chord diagram. This repository provides some code to draw self-loops nicely
Code for generating a single image pretraining dataset
Single Image Pretraining of Visual Representations As shown in the paper A critical analysis of self-supervision, or what we can learn from a single i
Video Contrastive Learning with Global Context
Video Contrastive Learning with Global Context (VCLR) This is the official PyTorch implementation of our VCLR paper. Install dependencies environments
Pytorch implementation of CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation"
MUST-GAN Code | paper The Pytorch implementation of our CVPR2021 paper "MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generat
A Code that can make your Discord Account 24/7!
Online-Forever Make your Discord Account Online 24/7! A Code written in Python that helps you to keep your account 24/7. The main.py is the main file.
ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.
ManiSkill-Learn ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge, a large-scale learning-from-dem
Implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
SemCo The official pytorch implementation of the paper All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
A library for uncertainty representation and training in neural networks.
Epistemic Neural Networks A library for uncertainty representation and training in neural networks. Introduction Many applications in deep learning re
Implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning"
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning This is the implementation of the paper "Self-Promoted Prototype Refinement
Object-aware Contrastive Learning for Debiased Scene Representation
Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo
Universal End2End Training Platform, including pre-training, classification tasks, machine translation, and etc.
背景 安装教程 快速上手 (一)预训练模型 (二)机器翻译 (三)文本分类 TenTrans 进阶 1. 多语言机器翻译 2. 跨语言预训练 背景 TrenTrans是一个统一的端到端的多语言多任务预训练平台,支持多种预训练方式,以及序列生成和自然语言理解任务。 安装教程 git clone git
A collection of interactive machine-learning experiments: 🏋️models training + 🎨models demo
🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
MASS: Masked Sequence to Sequence Pre-training for Language Generation
MASS: Masked Sequence to Sequence Pre-training for Language Generation
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps
Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s
A Structured Self-attentive Sentence Embedding
Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR
A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.
Use this instead: https://github.com/facebookresearch/maskrcnn-benchmark A Pytorch Implementation of Detectron Example output of e2e_mask_rcnn-R-101-F
Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)
Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"
GAN stability This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converg
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
ReSSL: Relational Self-Supervised Learning with Weak Augmentation This repository contains PyTorch evaluation code, training code and pretrained model
This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"
Polarized Self-Attention: Towards High-quality Pixel-wise Regression This is an official implementation of: Huajun Liu, Fuqiang Liu, Xinyi Fan and Don
Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation", Haoxiang Wang, Han Zhao, Bo Li.
Bridging Multi-Task Learning and Meta-Learning Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Trainin
Spatial Contrastive Learning for Few-Shot Classification (SCL)
This repo contains the official implementation of Spatial Contrastive Learning for Few-Shot Classification (SCL), which presents of a novel contrastive learning method applied to few-shot image classification in order to learn more general purpose embeddings, and facilitate the test-time adaptation to novel visual categories.
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020)
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
Deduplicating Training Data Makes Language Models Better
Deduplicating Training Data Makes Language Models Better This repository contains code to deduplicate language model datasets as descrbed in the paper
Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms
LESA Introduction This repository contains the official implementation of Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Cont
Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.
This repository is a toolkit to do machine learning for programming languages. It implements tokenization, dataset preprocessing, model training and m
A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.
ViTGAN: Training GANs with Vision Transformers A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers. Refer
An implementation of DeepMind's Relational Recurrent Neural Networks in PyTorch.
relational-rnn-pytorch An implementation of DeepMind's Relational Recurrent Neural Networks (Santoro et al. 2018) in PyTorch. Relational Memory Core (
A certifiable defense against adversarial examples by training neural networks to be provably robust
DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
ClusterGCN ⠀⠀ A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019). A
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
Self-training for Few-shot Transfer Across Extreme Task Differences
Self-training for Few-shot Transfer Across Extreme Task Differences (STARTUP) Introduction This repo contains the official implementation of the follo
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"
Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N
EsViT: Efficient self-supervised Vision Transformers
Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking Datasets You can download datasets that have been pre-pr
Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.
MidiBERT-Piano Authors: Yi-Hui (Sophia) Chou, I-Chun (Bronwin) Chen Introduction This is the official repository for the paper, MidiBERT-Piano: Large-
AlphaNet Improved Training of Supernet with Alpha-Divergence
AlphaNet: Improved Training of Supernet with Alpha-Divergence This repository contains our PyTorch training code, evaluation code and pretrained model
official implemntation for "Contrastive Learning with Stronger Augmentations"
CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun
Self-supervised Deep LiDAR Odometry for Robotic Applications
DeLORA: Self-supervised Deep LiDAR Odometry for Robotic Applications Overview Paper: link Video: link ICRA Presentation: link This is the correspondin
ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.
Accelerate PyTorch models with ONNX Runtime
Решения, подсказки, тесты и утилиты для тренировки по алгоритмам от Яндекса.
Решения и подсказки к тренировке по алгоритмам от Яндекса Что есть внутри Решения с подсказками и комментариями; рекомендую сначала смотреть md файл п
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)
S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th
A self hosted slack bot to conduct standups & generate reports.
StandupMonkey A self hosted slack bot to conduct standups & generate reports. Report Bug · Request Feature Installation Install already hosted bot (Us
IJCAI2020 & IJCV 2020 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo
Seg_Uncertainty In this repo, we provide the code for the two papers, i.e., MRNet:Unsupervised Scene Adaptation with Memory Regularization in vivo, IJ
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Code accompanying CVPR'20 paper of the same title. Paper lin
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f
Self Driving RC Car Code
Derp Learning Derp Learning is a Python package that collects data, trains models, and then controls an RC car for track racing. Hardware You will nee
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
HyperPose is a library for building high-performance custom pose estimation applications.
HyperPose is a library for building high-performance custom pose estimation applications.
Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.
S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi
2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation
2021-MICCAI-Progressively Normalized Self-Attention Network for Video Polyp Segmentation Authors: Ge-Peng Ji*, Yu-Cheng Chou*, Deng-Ping Fan, Geng Che
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'
SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline
Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).
Self-supervised Graph-level Representation Learning with Local and Global Structure Introduction This project is an implementation of ``Self-supervise
Piotr - IoT firmware emulation instrumentation for training and research
Piotr: Pythonic IoT exploitation and Research Introduction to Piotr Piotr is an emulation helper for Qemu that provides a convenient way to create, sh
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang
Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.
WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can be initiated without requiring that all the processes enter it. It partially reduces the data within non-overlapping groups of process, improving the parallel scalability.
Bagua is a flexible and performant distributed training algorithm development framework.
Bagua is a flexible and performant distributed training algorithm development framework.
Heimdall watchtower automatically sends you emails to notify you of the latest progress of your deep learning programs.
This software automatically sends you emails to notify you of the latest progress of your deep learning programs.
Spectral Tensor Train Parameterization of Deep Learning Layers
Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr
Python library to decrypt Airtag reports, as well as a InfluxDB/Grafana self-hosted dashboard example
Openhaystack-python This python daemon will allow you to gather your Openhaystack-based airtag reports and display them on a Grafana dashboard. You ca
Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".
Dual Self-Attention Network for Multivariate Time Series Forecasting 20.10.26 Update: Due to the difficulty of installation and code maintenance cause
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"
Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat
Shared code for training sentence embeddings with Flax / JAX
flax-sentence-embeddings This repository will be used to share code for the Flax / JAX community event to train sentence embeddings on 1B+ training pa
Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021
PLOME:Pre-training with Misspelled Knowledge for Chinese Spelling Correction (ACL2021) This repository provides the code and data of the work in ACL20
This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.
KGI (Knowledge Graph Induction) for slot filling This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code fo
Official code of paper "PGT: A Progressive Method for Training Models on Long Videos" on CVPR2021
PGT Code for paper PGT: A Progressive Method for Training Models on Long Videos. Install Run pip install -r requirements.txt. Run python setup.py buil
This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations at CVPR'21. According to some product reasons, we are not planning to release the training/testing codes and models. However, we will release the dataset and the scripts to prepare the dataset.
TransFill-Reference-Inpainting This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transf
The source codes for ACL 2021 paper 'BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data'
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data This repository provides the implementation details for
Meltano: ELT for the DataOps era. Meltano is open source, self-hosted, CLI-first, debuggable, and extensible.
Meltano is open source, self-hosted, CLI-first, debuggable, and extensible. Pipelines are code, ready to be version c
YoloV5 implemented by TensorFlow2 , with support for training, evaluation and inference.
Efficient implementation of YOLOV5 in TensorFlow2
nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.
What is nnDetection? Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of hi
Orthogonal Over-Parameterized Training
The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere. See our previous work -- MHE for an in-depth introduction.
quantize aware training package for NCNN on pytorch
ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption, CVPR 2021 (Oral)
TAP: Text-Aware Pre-training TAP: Text-Aware Pre-training for Text-VQA and Text-Caption by Zhengyuan Yang, Yijuan Lu, Jianfeng Wang, Xi Yin, Dinei Flo
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering Jang Hyun Cho1, Utkarsh Mall2, Kavita Bala2, Bharath Harihar
Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.
One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi
Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation"
EgoNet Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation". This repo inclu