collection of diffusion model papers categorized by their subareas
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Updated
May 29, 2024
collection of diffusion model papers categorized by their subareas
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
This repository contains a collection of papers and codes for continual reinforcement learning.
Avalanche: an End-to-End Library for Continual Learning based on PyTorch.
[ACL2024] A Codebase for Incremental Learning with Large Language Models; Official released code for "Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models (ACL 2024)", "Incremental Sequence Labeling: A Tale of Two Shifts (ACL 2024 Findings)", and "Concept-1K: A Novel Benchmark for Instance Incremental Learning (arxiv)"
Code for the paper "FOCIL: Finetune-and-Freeze for Online Class-Incremental Learning by Training Randomly Pruned Sparse Experts"
Dans mon défi de développement et d'amélioration quotidienne, je vous présente "One Commit Per Day". Ce projet va au-delà d'un simple dépôt GitHub ; il incarne mon engagement quotidien envers l'amélioration continue de mes compétences en programmation. Chaque jour, un nouveau commit marque ma progression continue et ma passion pour le codage.
Official PyTorch implementation of paper "Continual Action Assessment via Task-Consistent Score-Discriminative Feature Distribution Modeling" (TCSVT 2024)
Continual Learning of Large Language Models: A Comprehensive Survey
AGILE NeurIPS 2023 cleaned version
Forecasting System for Continual Learning Scenarios based on Hoeffding Trees With Change Point Detection Mechanism
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
Continual Relation Extraction
This website applies a recommendation system and continuous learning.
Awesome Machine Unlearning (A Survey of Machine Unlearning)
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
A library for benchmarking the Long Term Memory and Continual learning capabilities of LLM based agents. With all the tests and code you need to evaluate your own agents. See more in the blogpost:
Code for CPAL-2024 paper "Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates"
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