4 papers · 1 filter
Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning
Katarzyna Filus, Kamil Faber, Roberto Corizzo +1
Continual learning studies how models can adapt to new tasks while retaining previously acquired knowledge. Although a broad spectrum of methods has been proposed to mitigate catas…
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning
Md Yousuf Harun, Jhair Gallardo, Christopher Kanan
Out-of-distribution (OOD) detection and OOD generalization are widely studied in Deep Neural Networks (DNNs), yet their relationship remains poorly understood. We empirically show…
A Good Start Matters: Enhancing Continual Learning with Data-Driven Weight Initialization
Md Yousuf Harun, Christopher Kanan
To adapt to real-world data streams, continual learning (CL) systems must rapidly learn new concepts while preserving and utilizing prior knowledge. When it comes to adding new inf…
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?
Md Yousuf Harun, Kyungbok Lee, Jhair Gallardo +2
Embeddings produced by pre-trained deep neural networks (DNNs) are widely used; however, their efficacy for downstream tasks can vary widely. We study the factors influencing trans…