Official Repository for our ICCV2021 paper: Continual Learning on Noisy Data Streams via Self-Purified Replay

Related tags

Deep LearningSPR
Overview

Continual Learning on Noisy Data Streams via Self-Purified Replay

This repository contains the official PyTorch implementation for our ICCV2021 paper.

  • Chris Dongjoo Kim*, Jinseo Jeong*, Sangwoo Moon, Gunhee Kim. Continual Learning on Noisy Data Streams via Self-Purified Replay. In ICCV, 2021 (* equal contribution).

[Paper Link][Slides][Poster]

System Dependencies

  • Python >= 3.6.1
  • CUDA >= 9.0 supported GPU

Installation

Using virtual env is recommended.

$ conda create --name SPR python=3.6

Install pytorch==1.7.0 and torchvision==0.8.1. Then, install the rest of the requirements.

$ pip install -r requirements.txt

Data and Log directory set-up

create checkpoints and data directories. We recommend symbolic links as below.

$ mkdir data
$ ln -s [MNIST Data Path] data/mnist
$ ln -s [CIFAR10 Data Path] data/cifar10
$ ln -s [CIFAR100 Data Path] data/cifar100
$ ln -s [Webvision Data Path] data/webvision

$ ln -s [log directory path] checkpoints

Run

Specify parameters in config yaml, episodes yaml files.

python main.py --log-dir [log directory path] --c [config file path] --e [episode file path] --override "|" --random_seed [seed]

# e.g. to run mnist symmetric noise 40% experiment,
python main.py --log-dir [log directory path] --c configs/mnist_spr.yaml --e episodes/mnist-split_epc1_a.yaml --override "corruption_percent=0.4";

# e.g. to run cifar10 asymmetric noise 40% experiment,
python main.py --log-dir [log directory path] --c configs/cifar10_spr.yaml --e episodes/cifar10-split_epc1_asym_a.yaml --override "asymmetric_nosie=False|corruption_percent=0.4";

# e.g. to run cifar100 superclass symmetric noise 40% experiment,
python main.py --log-dir [log directory path] --c configs/cifar100_spr.yaml --e episodes/cifar100sup-split_epc1_a.yaml --override "superclass_nosie=True|corruption_percent=0.4";

Expert Parallel Training

If you use slurm environment, training expert models in advance is possible.

# e.g. to run mnist symmetric noise 40% experiment,
python meta-main.py --log-dir [log directory path] -c configs/mnist_spr.yaml -e episodes/mnist-split_epc1_a.yaml --random_seed [seed] --override "corruption_percent=0.4" --njobs 10 --jobs_per_gpu 3

# also, you can only train experts for later use by adding an --expert_train_only option.
python meta-main.py --log-dir [log directory path] -c configs/mnist_spr.yaml -e episodes/mnist-split_epc1_a.yaml --random_seed [seed] --override "corruption_percent=0.4" --ngpu 10 --jobs_per_gpu 3 --expert_train_only

## to use the trained experts, set the same [log directory path] and [seed].
python main.py --log-dir [log directory path] --c configs/mnist_spr.yaml --e episodes/mnist-split_epc1_a.yaml --random_seed [seed] --override "corruption_percent=0.4";

Citation

The code and dataset are free to use for academic purposes only. If you use any of the material in this repository as part of your work, we ask you to cite:

@inproceedings{kim-ICCV-2021,
    author    = {Chris Dongjoo Kim and Jinseo Jeong and Sangwoo Moon and Gunhee Kim},
    title     = "{Continual Learning on Noisy Data Streams via Self-Purified Replay}"
    booktitle = {ICCV},
    year      = 2021
}

Last edit: Oct 12, 2021

Owner
Jinseo Jeong
graduate student @ vision & learning lab, Seoul National Univ.
Jinseo Jeong
The official repository for BaMBNet

BaMBNet-Pytorch Paper

Junjun Jiang 18 Dec 04, 2022
Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems

AequeVox Replication Package for AequeVox:Automated Fariness Testing for Speech Recognition Systems README under development. Python Packages Required

Sai Sathiesh 2 Aug 28, 2022
Data and analysis code for an MS on SK VOC genomes phenotyping/neutralisation assays

Description Summary of phylogenomic methods and analyses used in "Immunogenicity of convalescent and vaccinated sera against clinical isolates of ance

Finlay Maguire 1 Jan 06, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
A Python framework for developing parallelized Computational Fluid Dynamics software to solve the hyperbolic 2D Euler equations on distributed, multi-block structured grids.

pyHype: Computational Fluid Dynamics in Python pyHype is a Python framework for developing parallelized Computational Fluid Dynamics software to solve

Mohamed Khalil 21 Nov 22, 2022
A programming language written with python

Kaoft A programming language written with python How to use A simple Hello World: c="Hello World" c Output: "Hello World" Operators: a=12

1 Jan 24, 2022
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 04, 2023
Bootstrapped Unsupervised Sentence Representation Learning (ACL 2021)

Install first pip3 install -e . Training python3 training/unsupervised_tuning.py python3 training/supervised_tuning.py python3 training/multilingual_

yanzhang_nlp 26 Jul 22, 2022
implicit displacement field

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
This project uses Template Matching technique for object detecting by detection of template image over base image.

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 7 May 29, 2022
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
TensorLight - A high-level framework for TensorFlow

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced feature

Benjamin Kan 10 Jul 31, 2022
Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs at the moment, Cycles and Arnold supported

GafferHaven Plugin for Gaffer providing direct acess to asset from PolyHaven.com. Only HDRIs are supported at the moment, in Cycles and Arnold lights.

Jakub Vondra 6 Jan 26, 2022
[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation

Self-Supervised Learning of Image Scale and Orientation Estimation (BMVC 2021) This is the official implementation of the paper "Self-Supervised Learn

Jongmin Lee 17 Nov 10, 2022
Contrastively Disentangled Sequential Variational Audoencoder

Contrastively Disentangled Sequential Variational Audoencoder (C-DSVAE) Overview This is the implementation for our C-DSVAE, a novel self-supervised d

Junwen Bai 35 Dec 24, 2022
SberSwap Video Swap base on deep learning

SberSwap Video Swap base on deep learning

Sber AI 431 Jan 03, 2023
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Official implementation of Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D usi

Visual Intelligence and Systems Group 441 Dec 20, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
Least Square Calibration for Peer Reviews

Least Square Calibration for Peer Reviews Requirements gurobipy - for solving convex programs GPy - for Bayesian baseline numpy pandas To generate p

Sigma <a href=[email protected]"> 1 Nov 01, 2021