Python Noisy-dqn Resources

LUPerson-NL Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL) The repository is for our CVPR2022 paper Large-Scale

Peer Loss functions This repository is the (Multi-Class & Deep Learning) Pytorch implementation of "Peer Loss Functions: Learning from Noisy Labels wi

Training-a-deep-learning-model-on-the-noisy-CIFAR-dataset This repository contai

Qcover is an open source effort to help exploring combinatorial optimization problems in Noisy Intermediate-scale Quantum(NISQ) processor. It is devel

VIMuRe Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. If you use this code please cite this article (preprint). De

Robust Contrastive Learning against Noisy Views This repository provides a PyTorch implementation of the Robust InfoNCE loss proposed in paper Robust

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

Learning with Noisy Labels via Sparse Regularization This repository is the official implementation of [Learning with Noisy Labels via Sparse Regulari

Do you want a RL agent nicely moving on Atari? Rainbow is all you need! This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains bo

Deep Reinforcement Learning in TensorFlow TensorFlow implementation of Deep Reinforcement Learning papers. This implementation contains: [1] Playing A

DeepRL This code implements the standard deep Q-learning and dueling network with experience replay (memory buffer) for playing simple games. DQN algo

Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries, which are mainly based on

ElegantRL “小雅”: Scalable and Elastic Deep Reinforcement Learning ElegantRL is developed for researchers and practitioners with the following advantage

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks This is the code for the paper: MentorNet: Learning Data-Driven Curriculum fo

ChainerRL and PFRL ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement al

Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels The implementation of Hypernet-Ensemble Le

Deep Q-Learning Recommend papers The first step is to read and understand the method that you will implement. It was first introduced in a 2013 paper

Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data. This is the code of CVPR 2019 paper《Unequal Training for Deep Face Recognition

Meta-Weight-Net NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels). The

This is code of RID-Noise. Reproduce RID-Noise Results Toy tasks Please refer to the notebook ridnoise.ipynb to view experiments on three toy tasks. B

Deconfounding Temporal Autoencoder (DTA) This is a repository for the paper "Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Tim

Rainbow 🌈 An implementation of Rainbow DQN which reaches a median HNS of 205.7 after only 10M frames (the original Rainbow from Hessel et al. 2017 re

PGDF This repo is the official implementation of our paper "Sample Prior Guided Robust Model Learning to Suppress Noisy Labels ". Citation If you use

A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=

WarPI The official PyTorch implemention of our paper "Learning to Rectify for Robust Learning with Noisy Labels". Run python main.py --corruption_type

cleanlab is the data-centric ML ops package for machine learning with noisy labels. cleanlab cleans labels and supports finding, quantifying, and lear

DivNoising: Diversity Denoising with Fully Convolutional Variational Autoencoders Mangal Prakash1, Alexander Krull1,2, Florian Jug2 1Authors contribut

Rainbow 🌈 An implementation of Rainbow DQN which outperforms the paper's (Hessel et al. 2017) results on 40% of tested games while using 20x less dat

Automatic, Readable, Reusable, Extendable Machin is a reinforcement library designed for pytorch. Build status Platform Status Linux Windows Supported

Dataloader for CIFAR-N CIFAR-10N noise_label = torch.load('./data/CIFAR-10_human.pt') clean_label = noise_label['clean_label'] worst_label = noise_lab

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

doubtlab A lab for bad labels. Warning still in progress. This repository contains general tricks that may help you find bad, or noisy, labels in your

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"

WARPd Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction fro

Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel

JumpDetectR Name of QuantLet : JumpDetectR Published in : 'To be published as "Jump dynamics in high frequency crypto markets"' Description : 'Scala

AI Plays Flappy Bird I've trained a DQN that learns to play flappy bird on it's own. Try the pre-trained model First install the pip requirements and

Exploring Munchausen Reinforcement Learning This is the project repository of my team in the "Advanced Deep Learning for Robotics" course at TUM. Our

Continual Learning on Noisy Data Streams via Self-Purified Replay This repository contains the official PyTorch implementation for our ICCV2021 paper.

A GOOD REPRESENTATION DETECTS NOISY LABELS This code is a PyTorch implementation of the paper: Prerequisites Python 3.6.9 PyTorch 1.7.1 Torchvision 0.

Kun Liu*, Yao Fu*, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang, Sheng Gao. Noisy-Labeled NER with Confidence Estimation. NAACL 2021. [arxiv]

BasicRL: easy and fundamental codes for deep reinforcement learning BasicRL is an improvement on rainbow-is-all-you-need and OpenAI Spinning Up. It is

SNIPS: Solving Noisy Inverse Problems Stochastically This repo contains the official implementation for the paper SNIPS: Solving Noisy Inverse Problem

A curated (most recent) list of resources for Learning with Noisy Labels

PyTorch Project Template is being sponsored by the following tool; please help to support us by taking a look and signing up to a free trial PyTorch P

With DataQA, you can label unstructured text documents using rule-based distant supervision.

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

Human-Level Control through Deep Reinforcement Learning Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning. This imp

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

DQN-Trading This is a framework based on deep reinforcement learning for stock market trading. This project is the implementation code for the two pap

Channel LM Prompting (and beyond) This includes an original implementation of Sewon Min, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. "Noisy Cha

Flood Detection Challenge This repository contains code for our submission to the ETCI 2021 Competition on Flood Detection (Winning Solution #2). Acco

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng Prerequisites We have tested the code on Ubun

Noisy Natural Gradient as Variational Inference PyTorch implementation of Noisy Natural Gradient as Variational Inference. Requirements Python 3 Pytor

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

STEAL This is the official inference code for: Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations David Acuna, Amlan Kar, Sanj

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

2021-CVPR-MvCLN This repo contains the code and data of the following paper accepted by CVPR 2021 Partially View-aligned Representation Learning with

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. 🔥

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

Nested-Co-teaching (L2ID@CVPR2021) Pytorch implementation of paper "Boosting Co-teaching with Compression Regularization for Label Noise" [PDF] If our

Paddle-RLBooks Welcome to Paddle-RLBooks which is a reinforcement learning code study guide based on pure PaddlePaddle. 欢迎来到Paddle-RLBooks，该仓库主要是针对强化学

Reinforcement Learning (PyTorch) 🤖 + 🍰 = ❤️ This repo will contain PyTorch implementation of various fundamental RL algorithms. It's aimed at making

Trove Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers

Reinforcement Learning for Finance We apply reinforcement learning for stock trading. Fetch Data Example import utils # fetch symbols from yahoo fina

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Contrast to Divide: self-supervised pre-training for learning with noisy labels This is an official implementation of "Contrast to Divide: self-superv

spatial-intention-maps This code release accompanies the following paper: Spatial Intention Maps for Multi-Agent Mobile Manipulation Jimmy Wu, Xingyua

ChainerRL ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Ch

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning. TF-Agents makes implementing, de

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

Update for using DQN to play sekiro 2021.2.2（English Version） This is the code of using DQN to play Sekiro . I am very glad to tell that I have writen

SLM Lab Modular Deep Reinforcement Learning framework in PyTorch. Documentation: https://slm-lab.gitbook.io/slm-lab/ BeamRider Breakout KungFuMaster M