Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks   We introduce PathGAN, a deep neural network for visual scanpath prediction
trained on adversarial examples. A visual scanpath is defined as the sequence
of fixation points over an image defined by a human observer with its gaze.
PathGAN is composed of two parts, the generator and the discriminator. Both
parts extract features from images using off-the-shelf networks, and train
recurrent layers to generate or discriminate scanpaths accordingly. In scanpath
prediction, the stochastic nature of the data makes it very difficult to
generate realistic predictions using supervised learning strategies, but we
adopt adversarial training as a suitable alternative. Our experiments prove how
PathGAN improves the state of the art of visual scanpath prediction on the iSUN
and Salient360! datasets. Source code and models are available at
https://imatge-upc.github.io/pathgan/

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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an