19 citations · 21 across the 5 of their papers we have counts for
5 papers
Adaptive Transfer Learning: a simple but effective transfer learning
Jung H Lee, Henry J Kvinge, Scott Howland +7
Transfer learning (TL) leverages previously obtained knowledge to learn new tasks efficiently and has been used to train deep learning (DL) models with limited amount of data. When…
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
Henry Kvinge, Scott Howland, Nico Courts +9
The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabel…
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Elliott Skomski, Aaron Tuor, Andrew Avila +5
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on…
Explanatory Masks for Neural Network Interpretability
Lawrence Phillips, Garrett Goh, Nathan Hodas
Neural network interpretability is a vital component for applications across a wide variety of domains. In such cases it is often useful to analyze a network which has already been…
Metric-Based Few-Shot Learning for Video Action Recognition
Chris Careaga, Brian Hutchinson, Nathan Hodas +1
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gon…