Latest Articles for Catastrophic Forgetting

Introducing FedGTG to retain knowledge while learning in federated settings.

A new approach tackles challenges in image segmentation while retaining knowledge of old categories.

New methods tackle challenges in machine learning for improved performance.

New framework improves models' ability to answer time-sensitive questions.

FETCH improves memory use while maintaining accuracy in machine learning tasks.

A novel approach to enhance continual learning with prompts and knowledge distillation.

A new method that enhances LLM performance while reducing resource use.

A new theory reveals insights into continual learning and forgetting in AI.

A novel method improves learning new classes while retaining old knowledge.

Introducing NeST for better learning of new classes without forgetting old ones.

A new approach to assess model performance and knowledge retention.

A study on local and global approaches in continual learning algorithms.

Discover how continuous learning is transforming artificial intelligence and its applications.

EverAdapt framework addresses fault recognition in changing machine conditions.

Research focuses on efficient multilingual language models using Knowledge Distillation and Mixture of Experts.

A new method improves learning efficiency while preserving knowledge in federated learning systems.

A new method helps classify heart views without losing prior knowledge.

This paper presents Aggregated Self-Supervision to enhance incremental learning.

A new approach aims to improve active learning's resilience to attacks.

New methods enhance ASR models for multiple languages, preserving past knowledge.

CluMo helps models learn continuously in Visual Question Answering without forgetting past knowledge.

A new method improves language model capabilities without losing original knowledge.

This article discusses data augmentation methods for improving continual reinforcement learning agents.

CF-KAN enhances recommendation systems by overcoming forgetting and adapting to user preferences.

AutoVCL enhances continual learning strategies in AI to manage task complexities.

Prompt baking improves language model performance and knowledge retention.

A new model improves AI's ability to learn without forgetting.

A new method for improving keyword spotting while retaining learned knowledge.

Innovative synthetic data method improves medical imaging accuracy while protecting patient privacy.

AWF enhances semantic segmentation by preventing catastrophic forgetting in machine learning models.

This model improves AI learning while retaining past knowledge.

DriftNet mimics biological learning processes to enhance AI's ability to learn continuously.

Learn how robots adapt and retain knowledge through continual learning.

A novel approach to address memory issues in machine learning.

A new framework improves learning efficiency in online continual learning.

FlipClass offers a new method for better learning in Generalized Category Discovery.

Learn how computers adapt to new information while retaining past knowledge.

A look into lifelong learning for robots and its future.

Research focuses on improving methods for detecting realistic fake speech.

Learn how machines adapt to new classes without forgetting old knowledge.