13 citations · 32 across the 11 of their papers we have counts for
11 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…
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…
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…
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…
GRASP: A Rehearsal Policy for Efficient Online Continual Learning
Md Yousuf Harun, Jhair Gallardo, Junyu Chen +1
Continual learning (CL) in deep neural networks (DNNs) involves incrementally accumulating knowledge in a DNN from a growing data stream. A major challenge in CL is that non-statio…
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…
How Efficient Are Today's Continual Learning Algorithms?
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +1
Supervised Continual learning involves updating a deep neural network (DNN) from an ever-growing stream of labeled data. While most work has focused on overcoming catastrophic forg…
SIESTA: Efficient Online Continual Learning with Sleep
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +2
In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any dis…
Inner Cell Mass and Trophectoderm Segmentation in Human Blastocyst Images using Deep Neural Network
Md Yousuf Harun, Thomas Huang, Aaron T. Ohta
Embryo quality assessment based on morphological attributes is important for achieving higher pregnancy rates from in vitro fertilization (IVF). The accurate segmentation of the em…
Image Segmentation of Zona-Ablated Human Blastocysts
Md Yousuf Harun, M Arifur Rahman, Joshua Mellinger +5
Automating human preimplantation embryo grading offers the potential for higher success rates with in vitro fertilization (IVF) by providing new quantitative and objective measures…