Class Incremental Learning
209 papers with code • 6 benchmarks • 1 datasets
Incremental learning of a sequence of tasks when the task-ID is not available at test time.
Benchmarks
These leaderboards are used to track progress in Class Incremental Learning
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Libraries
Use these libraries to find Class Incremental Learning models and implementations
CIFAR-100
Subtasks
Latest papers with no code
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
DynaMMo achieves this without compromising performance, offering a cost-effective solution for continual learning in medical applications.
Brain-Inspired Continual Learning-Robust Feature Distillation and Re-Consolidation for Class Incremental Learning
Our framework, named Robust Rehearsal, addresses the challenge of catastrophic forgetting inherent in continual learning (CL) systems by distilling and rehearsing robust features.
Realistic Continual Learning Approach using Pre-trained Models
Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge.
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.
Future-Proofing Class Incremental Learning
Exemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable.
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
So we propose SNRO, which slightly shifts the features of new classes to remember old classes.
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session.
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data.
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
On the other hand, Semi-IPC learns a prototype for each class with unsupervised regularization, enabling the model to incrementally learn from partially labeled new data while maintaining the knowledge of old classes.
