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.
The approach used by Anderson et al. REF is treating network layers, or entire networks, as modules and combines pretrained modules with untrained modules, to learn the shift in distributions between data sets.
Beyond Fine Tuning: A Modular Approach to Learning on Small Data
ArXiv
ArXiv
Computer Science