Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer
  Learning   Cross-dataset transfer learning is an important problem in person
re-identification (Re-ID). Unfortunately, not too many deep transfer Re-ID
models exist for realistic settings of practical Re-ID systems. We propose a
purely deep transfer Re-ID model consisting of a deep convolutional neural
network and an autoencoder. The latent code is divided into metric embedding
and nuisance variables. We then utilize an unsupervised training method that
does not rely on co-training with non-deep models. Our experiments show
improvements over both the baseline and competitors' transfer learning models.

31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat