Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generation For Adaption: A GAN-Based Approach for 3D Domain Adaption
  with Point Cloud Data   Recent deep networks have achieved good performance on a variety of 3d points
classification tasks. However, these models often face challenges in "wild
tasks".There are considerable differences between the labeled training/source
data collected by one Lidar and unseen test/target data collected by a
different Lidar. Unsupervised domain adaptation (UDA) seeks to overcome such a
problem without target domain labels.Instead of aligning features between
source data and target data,we propose a method that use a Generative
adversarial network to generate synthetic data from the source domain so that
the output is close to the target domain.Experiments show that our approach
performs better than other state-of-the-art UDA methods in three popular 3D
object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain
3D objects classification.

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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: nlin
3
The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex