Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MisConv: Convolutional Neural Networks for Missing Data   Processing of missing data by modern neural networks, such as CNNs, remains a
fundamental, yet unsolved challenge, which naturally arises in many practical
applications, like image inpainting or autonomous vehicles and robots. While
imputation-based techniques are still one of the most popular solutions, they
frequently introduce unreliable information to the data and do not take into
account the uncertainty of estimation, which may be destructive for a machine
learning model. In this paper, we present MisConv, a general mechanism, for
adapting various CNN architectures to process incomplete images. By modeling
the distribution of missing values by the Mixture of Factor Analyzers, we cover
the spectrum of possible replacements and find an analytical formula for the
expected value of convolution operator applied to the incomplete image. The
whole framework is realized by matrix operations, which makes MisConv extremely
efficient in practice. Experiments performed on various image processing tasks
demonstrate that MisConv achieves superior or comparable performance to the
state-of-the-art methods.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th