Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Solving Traffic4Cast Competition with U-Net and Temporal Domain
  Adaptation   In this technical report, we present our solution to the Traffic4Cast 2021
Core Challenge, in which participants were asked to develop algorithms for
predicting a traffic state 60 minutes ahead, based on the information from the
previous hour, in 4 different cities. In contrast to the previously held
competitions, this year's challenge focuses on the temporal domain shift in
traffic due to the COVID-19 pandemic. Following the past success of U-Net, we
utilize it for predicting future traffic maps. Additionally, we explore the
usage of pre-trained encoders such as DenseNet and EfficientNet and employ
multiple domain adaptation techniques to fight the domain shift. Our solution
has ranked third in the final competition. The code is available at
https://github.com/jbr-ai-labs/traffic4cast-2021.

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The categories of this paper are: adap-org
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph