Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Learning Enabled by Multiscale Deep Neural Network
  Inversion   Deep Neural Networks (DNNs) provide state-of-the-art solutions in several
difficult machine perceptual tasks. However, their performance relies on the
availability of a large set of labeled training data, which limits the breadth
of their applicability. Hence, there is a need for new {\em semi-supervised
learning} methods for DNNs that can leverage both (a small amount of) labeled
and unlabeled training data. In this paper, we develop a general loss function
enabling DNNs of any topology to be trained in a semi-supervised manner without
extra hyper-parameters. As opposed to current semi-supervised techniques based
on topology-specific or unstable approaches, ours is both robust and general.
We demonstrate that our approach reaches state-of-the-art performance on the
SVHN ($9.82\%$ test error, with $500$ labels and wide Resnet) and CIFAR10
(16.38% test error, with 8000 labels and sigmoid convolutional neural network)
data sets.

17
The categories of this paper are: q-fin
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol