568 citations · 1.1k across the 8 of their papers we have counts for
9 papers
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…
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
Ryne Roady, Tyler L. Hayes, Ronald Kemker +2
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, dep…
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
Ronald Kemker, Utsav B. Gewali, Christopher Kanan
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing…
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
Ronald Kemker, Ryan Luu, Christopher Kanan
Recent advances in computer vision using deep learning with RGB imagery (e.g., object recognition and detection) have been made possible thanks to the development of large annotate…
Continual Lifelong Learning with Neural Networks: A Review
German I. Parisi, Ronald Kemker, Jose L. Part +2
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning,…
FearNet: Brain-Inspired Model for Incremental Learning
Ronald Kemker, Christopher Kanan
Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standa…
Measuring Catastrophic Forgetting in Neural Networks
Ronald Kemker, Marc McClure, Angelina Abitino +2
Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily…
High-Resolution Multispectral Dataset for Semantic Segmentation
Ronald Kemker, Carl Salvaggio, Christopher Kanan
Unmanned aircraft have decreased the cost required to collect remote sensing imagery, which has enabled researchers to collect high-spatial resolution data from multiple sensor mod…
Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
Ronald Kemker, Carl Salvaggio, Christopher Kanan
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semanti…