Latest Articles for Catastrophic Forgetting

Exploring how repetition enhances continual learning in changing environments.

New techniques improve learning in spiking neural networks while reducing memory needs.

Discover methods to help AI learn continuously without forgetting past knowledge.

An overview of strategies for continuous learning in artificial intelligence.

Introducing CLAMP, a new method to enhance continual learning across various domains.

A new method tackles forgetting and data shifts in machine learning models.

New method helps chatbots remember past knowledge while learning new tasks.

A new framework enhances federated learning and prevents forgetfulness in AI models.

AGILE uses attention mechanisms to improve continual learning and reduce forgetting.

A new architecture addresses challenges in continual learning and reduces catastrophic forgetting.

New methods tackle the challenge of catastrophic forgetting in AI learning.

A new method to improve learning retention in AI systems.

A method enhancing image classification for multiple objects over time.

New methods aim to improve machine learning by retaining knowledge while adapting to new tasks.

An analysis of factors influencing forgetting in machine learning.

Discover how language models learn continuously and retain knowledge over time.

A new approach to improve video object segmentation performance across diverse data sources.

New method addresses challenges in machine learning without labels.

A new method to enhance exemplar-free continual learning by tracking class representation changes.

A new method for federated learning that addresses continual learning challenges.

Forgetting enhances learning in humans and machine models, improving adaptability and performance.

Exploring memory formation and learning processes in biological and artificial systems.

New framework enables efficient learning of diseases without storing past data.

A new method improves continual learning in AI by reducing forgetting.

Examining how LLMs adapt and learn continuously through internal and external knowledge.

A new method improves data learning in streaming environments.

A new method improves Large Language Models' ability to forget sensitive information.

Research focuses on minimizing forgetting in continual learning through theoretical bounds.

New approaches to improve memory retention in artificial intelligence.

A new method improves machine learning model adaptability in dynamic situations.

MIGU enhances continuous learning in language models without needing old data.

RAIL merges continual learning with vision-language models for better adaptability.

This study examines how model size affects performance in Online Continual Learning.

LEMoE offers efficient updates for large language models, addressing key challenges.

A new method improves continual learning in artificial intelligence with limited memory.

CLIP-CITE enhances CLIP models for specialized tasks while retaining flexibility.

pFedDIL improves machine learning by retaining knowledge while adapting to new tasks.

Introducing a method that enhances learning from limited data without forgetting past knowledge.

A study on using predictive uncertainty to reduce catastrophic forgetting in machine learning models.

Learnable Drift Compensation improves model performance in continual learning.