Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Collaborative Training of Balanced Random Forests for Open Set Domain
  Adaptation   In this paper, we introduce a collaborative training algorithm of balanced
random forests with convolutional neural networks for domain adaptation tasks.
In real scenarios, most domain adaptation algorithms face the challenges from
noisy, insufficient training data and open set categorization. In such cases,
conventional methods suffer from overfitting and fail to successfully transfer
the knowledge of the source to the target domain. To address these issues, the
following two techniques are proposed. First, we introduce the optimized
decision tree construction method with convolutional neural networks, in which
the data at each node are split into equal sizes while maximizing the
information gain. It generates balanced decision trees on deep features because
of the even-split constraint, which contributes to enhanced discrimination
power and reduced overfitting problem. Second, to tackle the domain
misalignment problem, we propose the domain alignment loss which penalizes
uneven splits of the source and target domain data. By collaboratively
optimizing the information gain of the labeled source data as well as the
entropy of unlabeled target data distributions, the proposed CoBRF algorithm
achieves significantly better performance than the state-of-the-art methods.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph