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. 🔥

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
JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python

jumpdiff jumpdiff is a python library with non-parametric Nadaraya─Watson estimators to extract the parameters of jump-diffusion processes. With jumpd

Rydin 28 Dec 10, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
StellarGraph - Machine Learning on Graphs

StellarGraph Machine Learning Library StellarGraph is a Python library for machine learning on graphs and networks. Table of Contents Introduction Get

S T E L L A R 2.6k Jan 05, 2023
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021.

Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection This repository is an official implementation of the AAAI 2021 paper Co-mi

MEGVII Research 20 Dec 07, 2022
FishNet: One Stage to Detect, Segmentation and Pose Estimation

FishNet FishNet: One Stage to Detect, Segmentation and Pose Estimation Introduction In this project, we combine target detection, instance segmentatio

1 Oct 05, 2022
General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends)

General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usec

The Kompute Project 1k Jan 06, 2023
Repository providing a wide range of self-supervised pretrained models for computer vision tasks.

Hierarchical Pretraining: Research Repository This is a research repository for reproducing the results from the project "Self-supervised pretraining

Colorado Reed 53 Nov 09, 2022
Official codebase for Pretrained Transformers as Universal Computation Engines.

universal-computation Overview Official codebase for Pretrained Transformers as Universal Computation Engines. Contains demo notebook and scripts to r

Kevin Lu 210 Dec 28, 2022
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
Language model Prompt And Query Archive

LPAQA: Language model Prompt And Query Archive This repository contains data and code for the paper How Can We Know What Language Models Know? Install

127 Dec 20, 2022
Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial

Computational Social Affective Neuroscience Laboratory 147 Jan 06, 2023
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

taganomaly Anomaly detection labeling tool, specifically for multiple time series (one time series per category). Taganomaly is a tool for creating la

Microsoft 272 Dec 17, 2022
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

Phil Wang 55 Jan 01, 2023
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Jihyun Lee 88 Nov 22, 2022
TriMap: Large-scale Dimensionality Reduction Using Triplets

TriMap TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet c

Ehsan Amid 235 Dec 24, 2022
Adaptive FNO transformer - official Pytorch implementation

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers This repository contains PyTorch implementation of the Adaptive Fourier Neu

NVIDIA Research Projects 77 Dec 29, 2022
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

KazuhitoTakahashi 217 Dec 12, 2022
Paaster is a secure by default end-to-end encrypted pastebin built with the objective of simplicity.

Follow the development of our desktop client here Paaster Paaster is a secure by default end-to-end encrypted pastebin built with the objective of sim

Ward 211 Dec 25, 2022
Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning Reference Abeßer, J. & Müller, M. Towards Audio Domain Adapt

Jakob Abeßer 2 Jul 06, 2022
Automatic Image Background Subtraction

Automatic Image Background Subtraction This repo contains set of scripts for automatic one-shot image background subtraction task using the following

Oleg Sémery 6 Dec 05, 2022