Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Ensembling GAN for Cross-Domain Semantic Segmentation   Deep neural networks (DNNs) have greatly contributed to the performance gains
in semantic segmentation. Nevertheless, training DNNs generally requires large
amounts of pixel-level labeled data, which is expensive and time-consuming to
collect in practice. To mitigate the annotation burden, this paper proposes a
self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain
data for semantic segmentation. In SE-GAN, a teacher network and a student
network constitute a self-ensembling model for generating semantic segmentation
maps, which together with a discriminator, forms a GAN. Despite its simplicity,
we find SE-GAN can significantly boost the performance of adversarial training
and enhance the stability of the model, the latter of which is a common barrier
shared by most adversarial training-based methods. We theoretically analyze
SE-GAN and provide an $\mathcal O(1/\sqrt{N})$ generalization bound ($N$ is the
training sample size), which suggests controlling the discriminator's
hypothesis complexity to enhance the generalizability. Accordingly, we choose a
simple network as the discriminator. Extensive and systematic experiments in
two standard settings demonstrate that the proposed method significantly
outperforms current state-of-the-art approaches. The source code of our model
will be available soon.

35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph