PyContinual (An Easy and Extendible Framework for Continual Learning)
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Updated
Jan 29, 2024 - Python
PyContinual (An Easy and Extendible Framework for Continual Learning)
A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).
Class-Incremental Learning: A Survey (TPAMI 2024)
Forward Compatible Few-Shot Class-Incremental Learning (CVPR'22)
Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need (IJCV 2024)
An Extensible Continual Learning Framework Focused on Language Models (LMs)
Towards increasing stability of neural networks for continual learning: https://arxiv.org/abs/2006.06958.pdf (NeurIPS'20)
Random memory adaptation model inspired by the paper: "Memory-based parameter adaptation (MbPA)"
A PyTorch implementation of the CVPR 2017 publication "Expert Gate: Lifelong Learning with a Network of Experts"
[IROS2022] Official repository of InCloud: Incremental Learning for Point Cloud Place Recognition, Published in IROS2022 https://arxiv.org/abs/2203.00807
A PyTorch implementation of the ECCV 2018 publication "Memory Aware Synapses: Learning what (not) to forget"
Implementation of "Episodic Memory in Lifelong Language Learning"(NeurIPS 2019) in Pytorch
Code for ECML/PKDD 2020 Paper --- Continual Learning with Knowledge Transfer for Sentiment Classification
The code repository for "Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks" (TPAMI 2023) in PyTorch.
This repository contains code and data of the paper **On the Limitations of Continual Learning for Malware Classification**, accepted to be published at the First Conference on Lifelong Learning Agents (CoLLAs).
Pre-training and Lifelong learning for User Embedding and Recommender System
Code for NeurIPS 2020 Paper --- Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
Continual Learning with Echo State Networks experiments
Source code for "Online Unsupervised Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions", ECCV 2022. This is the code has been implemented to perform training and evaluation of UDA approaches in continuous scenarios. The library has been implemented in PyTorch 1.7.1. Some newer versions should work as well.
Simulation code for Limbacher, T. and Legenstein, R. (2020). Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring
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