136 citations · 154 across the 8 of their papers we have counts for
8 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…
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Henry Kvinge, Zachary New, Nico Courts +6
Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limi…
Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary
Meg Pirrung, Nathan Hilliard, Artëm Yankov +4
Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user's mental model.…
Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Nathan Hilliard, Lawrence Phillips, Scott Howland +3
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architec…
Dynamic Input Structure and Network Assembly for Few-Shot Learning
Nathan Hilliard, Nathan O. Hodas, Courtney D. Corley
The ability to learn from a small number of examples has been a difficult problem in machine learning since its inception. While methods have succeeded with large amounts of traini…
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
Ark Anderson, Kyle Shaffer, Artem Yankov +2
In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically r…