Adaptive Transfer Learning: a simple but effective transfer learning

no code implementations • 22 Nov 2021 • Jung H Lee, Henry J Kvinge, Scott Howland, Zachary New, John Buckheit, Lauren A. Phillips, Elliott Skomski, Jessica Hibler, Courtney D. Corley, Nathan O. Hodas

Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.

Few-Shot Learning Transfer Learning

One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations

no code implementations • 2 Jun 2021 • Henry Kvinge, Scott Howland, Nico Courts, Lauren A. Phillips, John Buckheit, Zachary New, Elliott Skomski, Jung H. Lee, Sandeep Tiwari, Jessica Hibler, Courtney D. Corley, Nathan O. Hodas

We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.

Few-Shot Learning Out-of-Distribution Detection