Latest Articles for Catastrophic Forgetting

Fed-CPrompt enhances federated continual learning while preserving user privacy.

A look into the importance of continual learning in AI systems.

MoP-CLIP improves learning models in changing data environments.

A new method enhances speech recognition technology without losing previously learned knowledge.

A new model offers efficient workload prediction in cloud computing.

A new framework helps robots learn efficiently from limited data.

This study examines how to improve memory management in continual learning systems.

A new model promises better learning for artificial intelligence through brain-inspired methods.

Ada-QPacknet combines adaptive pruning and quantization for effective continual learning.

A study enhances ASR for older speakers, using innovative techniques.

Examining knowledge retention challenges in large language models during continuous training.

Study explores continual learning strategies for improving information retrieval systems.

A new method improves 3D reconstruction from a single image while preserving learned shapes.

A new method to help AI learn continuously without losing past knowledge.

A new method enhances federated learning by reducing data differences among clients.

A new approach to retain knowledge in graph data amidst continuous updates.

Researchers develop weight masks to help models retain knowledge while learning new tasks.

CLEVER model enhances information retrieval through efficient continual learning.

Examining how parameter isolation improves continual learning through dynamic sparse training methods.

New framework links Client Drift and Catastrophic Forgetting for better model performance.

Introducing metrics that account for task difficulty in continual learning assessments.

This framework addresses incremental learning in remote sensing with improved accuracy.

New method improves 3D modeling from 2D images while overcoming learning challenges.

Examining the trade-off between fine-tuning and preserving general abilities in AI models.

Study analyzes fine-tuning methods for language models to retain knowledge across languages.

New methods improve task learning and retention in dialogue systems.

New method BAdam improves continual learning for robots, retaining previous knowledge while learning new tasks.

A method to improve continual learning in machine learning through streaming data.

Analyzing fine-tuning effects and proposing conjugate prompting as a solution.

New methods improve alignment of language models with human values.

Improving continual learning by retaining knowledge using web data.

Research highlights catastrophic forgetting in multimodal language models post fine-tuning.

A clustering-based strategy helps machines learn continuously without losing prior knowledge.

New methods improve memory efficiency and accuracy in video object segmentation.

New methods improve adaptability of language models while retaining past knowledge.

A method that mimics human learning for better machine adaptability.

A new neural network model improves text recognition across different tasks and domains.

Introducing a flexible approach for machines to learn multiple tasks without forgetting.

t-DGR offers a new approach to continual learning, improving task retention and performance.

A new method enhances Transformers for better time series forecasting in limited data scenarios.