Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Convolution Filter Patterns for Transfer Learning   In this paper, we introduce a new regularization technique for transfer
learning. The aim of the proposed approach is to capture statistical
relationships among convolution filters learned from a well-trained network and
transfer this knowledge to another network. Since convolution filters of the
prevalent deep Convolutional Neural Network (CNN) models share a number of
similar patterns, in order to speed up the learning procedure, we capture such
correlations by Gaussian Mixture Models (GMMs) and transfer them using a
regularization term. We have conducted extensive experiments on the CIFAR10,
Places2, and CMPlaces datasets to assess generalizability, task
transferability, and cross-model transferability of the proposed approach,
respectively. The experimental results show that the feature representations
have efficiently been learned and transferred through the proposed statistical
regularization scheme. Moreover, our method is an architecture independent
approach, which is applicable for a variety of CNN architectures.

19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph