Title: Beyond Fine Tuning: A Modular Approach to Learning on Small Data
Authors: Aryk Anderson, Kyle Shaffer, Artem Yankov, Court Corley, Nathan Hodas
Keywords: Deep learning, Supervised Learning, Transfer Learning
TL;DR: A better way to do deep learning with small amounts of training data
Abstract:
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 rely on strategies such as fine-tuning a pre-trained neural network or the use of  domain-specific hand-engineered features. Here we take the  approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning. Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.  
Venue: Submitted to ICLR 2017