Continual Learning

921 papers with code • 29 benchmarks • 30 datasets

Continual Learning (also known as Incremental Learning, Life-long Learning) is a concept to learn a model for a large number of tasks sequentially without forgetting knowledge obtained from the preceding tasks, where the data in the old tasks are not available anymore during training new ones.
If not mentioned, the benchmarks here are Task-CL, where task-id is provided on validation.

Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Three scenarios for continual learning
Lifelong Machine Learning
Continual lifelong learning with neural networks: A review


Use these libraries to find Continual Learning models and implementations

Most implemented papers

Overcoming catastrophic forgetting in neural networks

ContinualAI/avalanche 2 Dec 2016

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.

Progressive Neural Networks

ContinualAI/avalanche 15 Jun 2016

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence.

Learning without Forgetting

ContinualAI/avalanche 29 Jun 2016

We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities.

Variational Continual Learning

nvcuong/variational-continual-learning ICLR 2018

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.

Three scenarios for continual learning

GMvandeVen/continual-learning 15 Apr 2019

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning difficult for machine learning.

Continual learning with hypernetworks

chrhenning/hypercl ICLR 2020

Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks.

On Tiny Episodic Memories in Continual Learning

facebookresearch/agem 27 Feb 2019

But for a successful knowledge transfer, the learner needs to remember how to perform previous tasks.

Meta-Learning Representations for Continual Learning

Khurramjaved96/mrcl NeurIPS 2019

We show that it is possible to learn naturally sparse representations that are more effective for online updating.

Continual Learning Through Synaptic Intelligence

ganguli-lab/pathint ICML 2017

While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning.

Continual Learning with Deep Generative Replay

ContinualAI/avalanche NeurIPS 2017

Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting.