/Learning to Learn: Model Regression Networks for Easy Small Sample Learning

Learning to Learn: Model Regression Networks for Easy Small Sample Learning

Yuxiong Wang and Martial Hebert
Conference Paper, European Conference on Computer Vision (ECCV), October, 2016

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


We develop a conceptually simple but powerful approach that can learn novel categories from few annotated examples. In this approach, the experience with already learned categories is used to facilitate the learning of novel classes. Our insight is two-fold: 1) there exists a generic, category agnostic transformation from models learned from few samples to models learned from large enough sample sets, and 2) such a transformation could be effectively learned by high-capacity regressors. In particular, we automatically learn the transformation with a deep model regression network on a large collection of model pairs. Experiments demonstrate that encoding this transformation as prior knowledge greatly facilitates the recognition in the small sample size regime on a broad range of tasks, including domain adaptation, fine-grained recognition, action recognition, and scene classification.

BibTeX Reference
author = {Yuxiong Wang and Martial Hebert},
title = {Learning to Learn: Model Regression Networks for Easy Small Sample Learning},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2016},
month = {October},