Demystifying How Self-Supervised Features Improve Training from Noisy Labels

Overview

Demystifying How Self-Supervised Features Improve Training from Noisy Labels

This code is a PyTorch implementation of the paper "[Demystifying How Self-Supervised Features Improve Training from Noisy Labels]". The code is run on the Tesla V-100.

Prerequisites

Python 3.8.5

PyTorch 1.7.1

Torchvision 0.9.0

Guideline

Downloading dataset:

Download the dataset from http://www.cs.toronto.edu/~kriz/cifar.html Put the dataset on data/

Get a SSL pre-trained model:

Run command below:

python main.py --batch_size 512 --epochs 1000 

Or download the pre-trained model from "this url" and put the pre-trained model in results/

CE with fixed encoder on symm. label noise:

Run command below:

python CE.py --simclr_pretrain --finetune_fc_only --noise_type symmetric --noise_rate 0.6 --epochs 150 

CE with fixed encoder on instance label noise (with down-sampling):

Run command below:

python CE.py --simclr_pretrain --down_sample --finetune_fc_only --noise_type instance --noise_rate 0.6 --epochs 150 

CE with regularizer on symm. label noise (random initialization):

Run command below:

python CE_Reg.py --reg rkd_dis --noise_type symmetric --noise_rate 0.6 --epochs 150 

References

The code of SSL pre-training is based on https://github.com/leftthomas/SimCLR

Owner
[email protected]
REsponsible & Accountable Learning (REAL) @ University of California, Santa Cruz
<a href=[email protected]">
Robustness between the worst and average case

Robustness between the worst and average case A repository that implements intermediate robustness training and evaluation from the NeurIPS 2021 paper

CMU Locus Lab 16 Dec 02, 2022
This repo in the implementation of EMNLP'21 paper "SPARQLing Database Queries from Intermediate Question Decompositions" by Irina Saparina, Anton Osokin

SPARQLing Database Queries from Intermediate Question Decompositions This repo is the implementation of the following paper: SPARQLing Database Querie

Yandex Research 20 Dec 19, 2022
[ICCV21] Official implementation of the "Social NCE: Contrastive Learning of Socially-aware Motion Representations" in PyTorch.

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VITA lab at EPFL 125 Dec 23, 2022
AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages This repository contains the code for the pa

Kelechi 40 Nov 24, 2022
MLJetReconstruction - using machine learning to reconstruct jets for CMS

MLJetReconstruction - using machine learning to reconstruct jets for CMS The C++ data extraction code used here was based heavily on that foundv here.

ALPhA Davidson 0 Nov 17, 2021
Convolutional Neural Network for Text Classification in Tensorflow

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convo

Denny Britz 5.5k Jan 02, 2023
공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

ObsCare_Main 소개 공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다. CCTV의 대수가 급격히 늘어나면서 관리와 효율성 문제와 더불어, 곳곳에 설치된 CCTV를 개별 관제하는 것으로는 응급 상

5 Jul 07, 2022
Artificial Neural network regression model to predict the energy output in a combined cycle power plant.

Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu

1 Feb 11, 2022
Machine learning and Deep learning models, deploy on telegram (the best social media)

Semi Intelligent BOT The project involves : Classifying fake news Classifying objects such as aeroplane, automobile, bird, cat, deer, dog, frog, horse

MohammadReza Norouzi 5 Mar 06, 2022
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning [CVPR'21, Oral] By Zhicheng Huang*, Zhaoyang Zeng*, Yupan H

Multimedia Research 196 Dec 13, 2022
To build a regression model to predict the concrete compressive strength based on the different features in the training data.

Cement-Strength-Prediction Problem Statement To build a regression model to predict the concrete compressive strength based on the different features

Ashish Kumar 4 Jun 11, 2022
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

Advanced Image Manipulation Lab @ Samsung AI Center Moscow 4.7k Dec 31, 2022
This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationships.

Auto-Lambda This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationship

Shikun Liu 76 Dec 20, 2022
LBK 26 Dec 28, 2022
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t

Martin Marek 6 Mar 03, 2022
Official Pytorch implementation of "DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network" (CVPR'21)

DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network Pytorch implementation for our DivCo. We propose a simple ye

64 Nov 22, 2022
FaRL for Facial Representation Learning

FaRL for Facial Representation Learning This repo hosts official implementation of our paper General Facial Representation Learning in a Visual-Lingui

Microsoft 19 Jan 05, 2022
ArcaneGAN by Alex Spirin

ArcaneGAN by Alex Spirin

Alex 617 Dec 28, 2022
TensorFlow (Python API) implementation of Neural Style

neural-style-tf This is a TensorFlow implementation of several techniques described in the papers: Image Style Transfer Using Convolutional Neural Net

Cameron 3.1k Jan 02, 2023
Background Matting: The World is Your Green Screen

Background Matting: The World is Your Green Screen By Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steve Seitz, and Ira Kemelmacher-Shlizerman Th

Soumyadip Sengupta 4.6k Jan 04, 2023