CL-Gym: Full-Featured PyTorch Library for Continual Learning

Related tags

Deep LearningCL-Gym
Overview

CL-Gym: Full-Featured PyTorch Library for Continual Learning

CL-Gym is a small yet very flexible library for continual learning research and development.
Currently, CL-Gym is under heavy development and ready to be used by experienced researchers and engineers. However, the stable version will be ready for public release in August. Meanwhile, we welcome your feedback and suggestions on making CL-Gym better for researchers and developers!

How to Install

pip install cl-gym

Getting Started

Documentation: https://cl-gym.readthedocs.io/en/main
Short Demo: Open In Colab

Reference

@InProceedings{Mirzadeh_2021_CVPR,
   author = {Mirzadeh, Seyed Iman and Ghasemzadeh, Hassan},
   title = {CL-Gym: Full-Featured PyTorch Library for Continual Learning},
   booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
   month = {June}, year = {2021}, pages = {3621-3627} }
You might also like...
ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners.
ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners.

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch. πŸ”₯

Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.

Trading Gym Trading Gym is an open-source project for the development of reinforcement learning algorithms in the context of trading. It is currently

Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX
Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX

coax is built on top of JAX, but it doesn't have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version.

gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks.
gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks.

gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks. It is built on top of the OpenAI Gym toolkit.

Deep Q Learning with OpenAI Gym and Pokemon Showdown

pokemon-deep-learning An openAI gym project for pokemon involving deep q learning. Made by myself, Sam Little, and Layton Webber. This code captures g

Multi-objective gym environments for reinforcement learning.
Multi-objective gym environments for reinforcement learning.

MO-Gym: Multi-Objective Reinforcement Learning Environments Gym environments for multi-objective reinforcement learning (MORL). The environments follo

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper)

CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (ACMMM'21 Oral Paper) (Accepted for oral presentation at ACM

ICSS - Interactive Continual Semantic Segmentation

Presentation This repository contains the code of our paper: Weakly-supervised c

[CVPR 2022] CoTTA Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation

CoTTA Code for our CVPR 2022 paper Continual Test-Time Domain Adaptation Prerequisite Please create and activate the following conda envrionment. To r

Comments
  • Missing Dataset PAMAP2

    Missing Dataset PAMAP2

    Dear Authors,

    I cannot find the link to your PAMAP2 Dataset as the link used in your implementation to download the dataset was not available. I have used the original PAMAP2 dataset from UCI link but it is not the same format as yours. Could you help me on this please? Thank you in advance for your help.

    opened by bonpagnakann 0
  • how to create my custom benchmark dataset ?

    how to create my custom benchmark dataset ?

    hello , how to create my custom benchmark dataset ? i am trying to use my own dataset for object detection task. i have two types of dataset and want to enter them in a sort of continual learning.

    but i am still new in the field of continual learning , and want to know how to config the benchmark

    looking forward tp your answer

    opened by alaa-shubbak 1
Releases(v1.0-beta.3)
  • v1.0-beta.3(Jun 23, 2021)

    Notes

    This is the first published version of CL-Gym. The current version needs more rigorous testing, and only then we will publish release candidates.

    Source code(tar.gz)
    Source code(zip)
Owner
Iman Mirzadeh
Graduate Research Assistant
Iman Mirzadeh
Opinionated code formatter, just like Python's black code formatter but for Beancount

beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b

Launch Platform 16 Oct 11, 2022
Official PyTorch implementation of PS-KD

Self-Knowledge Distillation with Progressive Refinement of Targets (PS-KD) Accepted at ICCV 2021, oral presentation Official PyTorch implementation of

61 Dec 28, 2022
Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!

Robust Video Matting (RVM) English | δΈ­ζ–‡ Official repository for the paper Robust High-Resolution Video Matting with Temporal Guidance. RVM is specific

flow-dev 2 Aug 21, 2022
[IROS'21] SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning

SurRoL IROS 2021 SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning Features dVRK compati

<a href=[email protected]"> 55 Jan 03, 2023
This repo contains implementation of different architectures for emotion recognition in conversations.

Emotion Recognition in Conversations Updates πŸ”₯ πŸ”₯ πŸ”₯ Date Announcements 03/08/2021 πŸŽ† πŸŽ† We have released a new dataset M2H2: A Multimodal Multiparty

Deep Cognition and Language Research (DeCLaRe) Lab 1k Dec 30, 2022
Task-related Saliency Network For Few-shot learning

Task-related Saliency Network For Few-shot learning This is an official implementation in Tensorflow of TRSN. Abstract An essential cue of human wisdo

1 Nov 18, 2021
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Convert Table data to approximate values with GUI

Table_Editor Convert Table data to approximate values with GUIs... usage - Import methods for extension Tables. Imported method supposed to have only

CLJ 1 Jan 10, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

Bayesian Deep Learning for Manufacturing 2.0 (dlmfg) Object Shape Error Response (OSER) Digital Lifecycle Management - In Process Quality Improvement

Sumit Sinha 30 Oct 31, 2022
PyTorch Implementation of Spatially Consistent Representation Learning(SCRL)

Spatially Consistent Representation Learning (CVPR'21) Official PyTorch implementation of Spatially Consistent Representation Learning (SCRL). This re

Kakao Brain 102 Nov 03, 2022
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)

A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022) https://arxiv.org/abs/2203.09388 Jianqi Ma, Zheto

MA Jianqi, shiki 104 Jan 05, 2023
CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

UC2 UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu,

Mingyang Zhou 28 Dec 30, 2022
A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".

Mugs: A Multi-Granular Self-Supervised Learning Framework This is a PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-

Sea AI Lab 62 Nov 08, 2022
PyTorch implementation of ECCV 2020 paper "Foley Music: Learning to Generate Music from Videos "

Foley Music: Learning to Generate Music from Videos This repo holds the code for the framework presented on ECCV 2020. Foley Music: Learning to Genera

Chuang Gan 30 Nov 03, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
MAVE: : A Product Dataset for Multi-source Attribute Value Extraction

MAVE: : A Product Dataset for Multi-source Attribute Value Extraction The dataset contains 3 million attribute-value annotations across 1257 unique ca

Google Research Datasets 89 Jan 08, 2023
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).

APPNP β € A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

Benedek Rozemberczki 329 Dec 30, 2022
The official MegEngine implementation of the ICCV 2021 paper: GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning

[ICCV 2021] GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning This is the official implementation of our ICCV2021 paper GyroFlow. Our pres

MEGVII Research 36 Sep 07, 2022
Array Camera Ptychography

Array Camera Ptychography This repository provides the code for the following papers: Schulz, Timothy J., David J. Brady, and Chengyu Wang. "Photon-li

Brady lab in Optical Sciences 1 Nov 15, 2021