Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Unsupervised Way to Understand Artifact Generating Internal Units in
  Generative Neural Networks   Despite significant improvements on the image generation performance of
Generative Adversarial Networks (GANs), generations with low visual fidelity
still have been observed. As widely used metrics for GANs focus more on the
overall performance of the model, evaluation on the quality of individual
generations or detection of defective generations is challenging. While recent
studies try to detect featuremap units that cause artifacts and evaluate
individual samples, these approaches require additional resources such as
external networks or a number of training data to approximate the real data
manifold. In this work, we propose the concept of local activation, and devise
a metric on the local activation to detect artifact generations without
additional supervision. We empirically verify that our approach can detect and
correct artifact generations from GANs with various datasets. Finally, we
discuss a geometrical analysis to partially reveal the relation between the
proposed concept and low visual fidelity.

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The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga