7 papers
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…
Unlocking ImageNet's Multi-Object Nature: Automated Large-Scale Multilabel Annotation
Junyu Chen, Md Yousuf Harun, Christopher Kanan
The original ImageNet benchmark enforces a single-label assumption, despite many images depicting multiple objects. This leads to label noise and limits the richness of the learnin…
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…
INSIGHT: Explainable Weakly-Supervised Medical Image Analysis
Wenbo Zhang, Junyu Chen, Christopher Kanan
Due to their large sizes, volumetric scans and whole-slide pathology images (WSIs) are often processed by extracting embeddings from local regions and then an aggregator makes pred…
Improving Multimodal Large Language Models Using Continual Learning
Shikhar Srivastava, Md Yousuf Harun, Robik Shrestha +1
Generative large language models (LLMs) exhibit impressive capabilities, which can be further augmented by integrating a pre-trained vision model into the original LLM to create a…
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…