Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised semantic segmentation needs strong, varied perturbations   Consistency regularization describes a class of approaches that have yielded
ground breaking results in semi-supervised classification problems. Prior work
has established the cluster assumption - under which the data distribution
consists of uniform class clusters of samples separated by low density regions
- as important to its success. We analyze the problem of semantic segmentation
and find that its' distribution does not exhibit low density regions separating
classes and offer this as an explanation for why semi-supervised segmentation
is a challenging problem, with only a few reports of success. We then identify
choice of augmentation as key to obtaining reliable performance without such
low-density regions. We find that adapted variants of the recently proposed
CutOut and CutMix augmentation techniques yield state-of-the-art
semi-supervised semantic segmentation results in standard datasets.
Furthermore, given its challenging nature we propose that semantic segmentation
acts as an effective acid test for evaluating semi-supervised regularizers.
Implementation at: https://github.com/Britefury/cutmix-semisup-seg.

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The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga