Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Neural Task Adaptation by Maximum Entropy Initialization   Transferring knowledge from one neural network to another has been shown to
be helpful for learning tasks with few training examples. Prevailing
fine-tuning methods could potentially contaminate pre-trained features by
comparably high energy random noise. This noise is mainly delivered from a
careless replacement of task-specific parameters. We analyze theoretically such
knowledge contamination for classification tasks and propose a practical and
easy to apply method to trap and minimize the contaminant. In our approach, the
entropy of the output estimates gets maximized initially and the first
back-propagated error is stalled at the output of the last layer. Our proposed
method not only outperforms the traditional fine-tuning, but also significantly
speeds up the convergence of the learner. It is robust to randomness and
independent of the choice of architecture. Overall, our experiments show that
the power of transfer learning has been substantially underestimated so far.

26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph