Continual Learning
817 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
AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees
Large language models (LLMs) are increasingly capable of completing knowledge intensive tasks by recalling information from a static pretraining corpus.
Realistic Continual Learning Approach using Pre-trained Models
Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge.
Remembering Transformer for Continual Learning
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task knowledge interferes with previously learned knowledge.
Learning to Classify New Foods Incrementally Via Compressed Exemplars
Therefore, food image classification systems should adapt to and manage data that continuously evolves.
Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts for Language-Guided Robot Manipulation
Our goal is to build embodied agents that can learn inductively generalizable spatial concepts in a continual manner, e. g, constructing a tower of a given height.
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net
These models use convolutional neural networks to reduce data dimensions effectively.
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.
