7 papers
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…
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…
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…
Overcoming the Stability Gap in Continual Learning
Md Yousuf Harun, Christopher Kanan
Pre-trained deep neural networks (DNNs) are being widely deployed by industry for making business decisions and to serve users; however, a major problem is model decay, where the D…