Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Saccading: Efficient Inference for Large Resolution Images   Image classification with deep neural networks is typically restricted to
images of small dimensionality such as 224 x 244 in Resnet models [24]. This
limitation excludes the 4000 x 3000 dimensional images that are taken by modern
smartphone cameras and smart devices. In this work, we aim to mitigate the
prohibitive inferential and memory costs of operating in such large dimensional
spaces. To sample from the high-resolution original input distribution, we
propose using a smaller proxy distribution to learn the co-ordinates that
correspond to regions of interest in the high-dimensional space. We introduce a
new principled variational lower bound that captures the relationship of the
proxy distribution's posterior and the original image's co-ordinate space in a
way that maximizes the conditional classification likelihood. We empirically
demonstrate on one synthetic benchmark and one real world large resolution DSLR
camera image dataset that our method produces comparable results with ~10x
faster inference and lower memory consumption than a model that utilizes the
entire original input distribution. Finally, we experiment with a more complex
setting using mini-maps from Starcraft II [56] to infer the number of
characters in a complex 3d-rendered scene. Even in such complicated scenes our
model provides strong localization: a feature missing from traditional
classification models.

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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc