Continual Learning
819 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.
Source:
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
Benchmarks
These leaderboards are used to track progress in Continual Learning
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Libraries
Use these libraries to find Continual Learning models and implementationsDatasets
CIFAR-10
CIFAR-100
MNIST
Oxford 102 Flower
Stanford Cars
Sketch
Permuted MNIST
CORe50
WikiArt
ROAD
Subtasks
Latest papers with no code
Toward industrial use of continual learning : new metrics proposal for class incremental learning
In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods.
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
This method aims to mitigate forgetting while adapting to new classes and domain shifts by combining the advantages of the Replay and Pseudo-Label methods and solving their limitations in the proposed scenario.
Hyperparameter Selection in Continual Learning
In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time.
On the Convergence of Continual Learning with Adaptive Methods
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the conceptualization of the plasticity-stability dilemma.
Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach
Despite their great success, the knowledge provided by the retrieval process is not always useful for improving the model prediction, since in some samples LLMs may already be quite knowledgeable and thus be able to answer the question correctly without retrieval.
Empowering Biomedical Discovery with AI Agents
We envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.
Continual Learning of Numerous Tasks from Long-tail Distributions
In this paper, we investigate the performance of continual learning algorithms with a large number of tasks drawn from a task distribution that is long-tail in terms of task sizes.
Continual Learning for Smart City: A Survey
We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.
Make Continual Learning Stronger via C-Flat
A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases.
Rehearsal-Free Modular and Compositional Continual Learning for Language Models
Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge.
