Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lightweight Unsupervised Domain Adaptation by Convolutional Filter
  Reconstruction   End-to-end learning methods have achieved impressive results in many areas of
computer vision. At the same time, these methods still suffer from a
degradation in performance when testing on new datasets that stem from a
different distribution. This is known as the domain shift effect. Recently
proposed adaptation methods focus on retraining the network parameters.
However, this requires access to all (labeled) source data, a large amount of
(unlabeled) target data, and plenty of computational resources. In this work,
we propose a lightweight alternative, that allows adapting to the target domain
based on a limited number of target samples in a matter of minutes rather than
hours, days or even weeks. To this end, we first analyze the output of each
convolutional layer from a domain adaptation perspective. Surprisingly, we find
that already at the very first layer, domain shift effects pop up. We then
propose a new domain adaptation method, where first layer convolutional filters
that are badly affected by the domain shift are reconstructed based on less
affected ones. This improves the performance of the deep network on various
benchmark datasets.

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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex