Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contraction Mapping of Feature Norms for Classifier Learning on the Data
  with Different Quality   The popular softmax loss and its recent extensions have achieved great
success in the deep learning-based image classification. However, the data for
training image classifiers usually has different quality. Ignoring such
problem, the correct classification of low quality data is hard to be solved.
In this paper, we discover the positive correlation between the feature norm of
an image and its quality through careful experiments on various applications
and various deep neural networks. Based on this finding, we propose a
contraction mapping function to compress the range of feature norms of training
images according to their quality and embed this contraction mapping function
into softmax loss or its extensions to produce novel learning objectives. The
experiments on various classification applications, including handwritten digit
recognition, lung nodule classification, face verification and face
recognition, demonstrate that the proposed approach is promising to effectively
deal with the problem of learning on the data with different quality and leads
to the significant and stable improvements in the classification accuracy.

21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg