Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Limitation of Convolutional Neural Networks in Recognizing
  Negative Images   Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance on a variety of computer vision tasks, particularly visual
classification problems, where new algorithms reported to achieve or even
surpass the human performance. In this paper, we examine whether CNNs are
capable of learning the semantics of training data. To this end, we evaluate
CNNs on negative images, since they share the same structure and semantics as
regular images and humans can classify them correctly. Our experimental results
indicate that when training on regular images and testing on negative images,
the model accuracy is significantly lower than when it is tested on regular
images. This leads us to the conjecture that current training methods do not
effectively train models to generalize the concepts. We then introduce the
notion of semantic adversarial examples - transformed inputs that semantically
represent the same objects, but the model does not classify them correctly -
and present negative images as one class of such inputs.

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The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-th