Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Adaptive Neural Networks for Object Recognition   We propose a simple neural network model to deal with the domain adaptation
problem in object recognition. Our model incorporates the Maximum Mean
Discrepancy (MMD) measure as a regularization in the supervised learning to
reduce the distribution mismatch between the source and target domains in the
latent space. From experiments, we demonstrate that the MMD regularization is
an effective tool to provide good domain adaptation models on both SURF
features and raw image pixels of a particular image data set. We also show that
our proposed model, preceded by the denoising auto-encoder pretraining,
achieves better performance than recent benchmark models on the same data sets.
This work represents the first study of MMD measure in the context of neural
networks.

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The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an