Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond Fine Tuning: A Modular Approach to Learning on Small Data   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.

25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat