PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

Overview

Contrast to Divide: self-supervised pre-training for learning with noisy labels

This is an official implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels". The code is based on DivideMix implementation.

Results

Following tables summarize main resutls of the paper:

CIFAR-10: CIFAR-10 results

CIFAR-100: CIFAR-100 results

Clothing1M: Clothing1M results

mini-WebVision: mini-WebVision

Running the code

First you need to install dependencies by running pip install -r requirements.txt.

You can download pretrained self-supervised models from Google Drive. Alternatively, you can train them by yourself, using SimCLR implementation. Put them into ./pretrained folder.

Then you can run the code for CIFAR

python3 main_cifar.py --r 0.8 --lambda_u 500 --dataset cifar100 --p_threshold 0.03 --data_path ./cifar-100 --experiment-name simclr_resnet18 --method selfsup --net resnet50

for Clothing1M

python3 main_clothing1M.py --data_path /path/to/clothing1m --experiment-name selfsup --method selfsup --p_threshold 0.7 --warmup 5 --num_epochs 120

or for mini-WebVision

python3 Train_webvision.py --p_threshold 0.03 --num_class 50 --data_path /path/to/webvision --imagenet_data_path /path/to/imagenet --method selfsup```

To run C2D with ELR+ just use the self-suprevised pretrained models with the original code.

License

This project is licensed under the terms of the MIT license.

An Open-Source Toolkit for Prompt-Learning.

An Open-Source Framework for Prompt-learning. Overview • Installation • How To Use • Docs • Paper • Citation • What's New? Nov 2021: Now we have relea

THUNLP 2.3k Jan 07, 2023
[ArXiv 2021] One-Shot Generative Domain Adaptation

GenDA - One-Shot Generative Domain Adaptation One-Shot Generative Domain Adaptation Ceyuan Yang*, Yujun Shen*, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Z

GenForce: May Generative Force Be with You 46 Dec 19, 2022
Stacked Recurrent Hourglass Network for Stereo Matching

SRH-Net: Stacked Recurrent Hourglass Introduction This repository is supplementary material of our RA-L submission, which helps reviewers to understan

28 Jan 03, 2023
Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash through feeding it pictures or videos.

Trash-Sorter-Extraordinaire Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash

Rameen Mahmood 1 Nov 07, 2021
TinyML Cookbook, published by Packt

TinyML Cookbook This is the code repository for TinyML Cookbook, published by Packt. Author: Gian Marco Iodice Publisher: Packt About the book This bo

Packt 93 Dec 29, 2022
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022
Centroid-UNet is deep neural network model to detect centroids from satellite images.

Centroid UNet - Locating Object Centroids in Aerial/Serial Images Introduction Centroid-UNet is deep neural network model to detect centroids from Aer

GIC-AIT 19 Dec 08, 2022
A PyTorch implementation of "From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network" (ICCV2021)

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network The official code of VisionLAN (ICCV2021). VisionLAN successfully a

81 Dec 12, 2022
Epidemiology analysis package

zEpid zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The purpose of this library is

Paul Zivich 111 Jan 08, 2023
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
deep-prae

Deep Probabilistic Accelerated Evaluation (Deep-PrAE) Our work presents an efficient rare event simulation methodology for black box autonomy using Im

Safe AI Lab 4 Apr 17, 2021
ECAENet (TensorFlow and Keras)

ECAENet: EfficientNet with Efficient Channel Attention for Plant Species Recognition (SCI:Q3) (Journal of Intelligent & Fuzzy Systems)

4 Dec 22, 2022
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift (ICCV 2021)

Π-NAS This repository provides the evaluation code of our submitted paper: Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training

Jiqi Zhang 18 Aug 18, 2022
Converting CPT to bert form for use

cpt-encoder 将CPT转成bert形式使用 说明 刚刚刷到又出了一种模型:CPT,看论文显示,在很多中文任务上性能比mac bert还好,就迫不及待想把它用起来。 根据对源码的研究,发现该模型在做nlu建模时主要用的encoder部分,也就是bert,因此我将这部分权重转为bert权重类型

黄辉 1 Oct 14, 2021
PyTorch Implementation of DSB for Score Based Generative Modeling. Experiments managed using Hydra.

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling This repository contains the implementation for the paper Diffusion

James Thornton 50 Jan 03, 2023
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
Code for "Unsupervised State Representation Learning in Atari"

Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This

Mila 217 Jan 03, 2023
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
Kaggle Ultrasound Nerve Segmentation competition [Keras]

Ultrasound nerve segmentation using Keras (1.0.7) Kaggle Ultrasound Nerve Segmentation competition [Keras] #Install (Ubuntu {14,16}, GPU) cuDNN requir

179 Dec 28, 2022