Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning in CNNs Using Filter-Trees   Convolutional Neural Networks (CNNs) are very effective for many pattern
recognition tasks. However, training deep CNNs needs extensive computation and
large training data. In this paper we propose Bank of Filter-Trees (BFT) as a
trans- fer learning mechanism for improving efficiency of learning CNNs. A
filter-tree corresponding to a filter in k^{th} convolu- tional layer of a CNN
is a subnetwork consisting of the filter along with all its connections to
filters in all preceding layers. An ensemble of such filter-trees created from
the k^{th} layers of many CNNs learnt on different but related tasks, forms the
BFT. To learn a new CNN, we sample from the BFT to select a set of filter
trees. This fixes the target net up to the k th layer and only the remaining
network would be learnt using train- ing data of new task. Through simulations
we demonstrate the effectiveness of this idea of BFT. This method constitutes a
novel transfer learning technique where transfer is at a sub- network level;
transfer can be effected from multiple source networks; and, with no finetuning
of the transferred weights, the performance achieved is on par with networks
that are trained from scratch.

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The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics