The study presents a new framework for Class-incremental learning (CIL), specifically for Long-Tailed Class-Incremental Learning (LT-CIL). The proposed Task-aware Expandable (TaE) framework dynamically allocates and updates task-specific trainable parameters, learning…
Continue readingThis paper presents a new privacy-preserving synthetic continual semantic segmentation framework for robotic surgery. The researchers address the problem of ‘catastrophic forgetting’ in Deep Neural Networks (DNNs), which refers to…
Continue readingThe paper presents an in-depth analysis of the joint effect of task similarity and overparameterization on catastrophic forgetting in continual learning. The researchers focus on two-task continual linear regression, where…
Continue readingThe research paper ‘Elephant Neural Networks: Born to be a Continual Learner’ by Qingfeng Lan and A. Rupam Mahmood, focuses on the issue of catastrophic forgetting in continual learning. The…
Continue readingThis paper introduces HePCo, a novel approach for Continual Federated Learning (CFL) that addresses the problem of catastrophic forgetting and data heterogeneity across clients without requiring access to any stored…
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