Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating Object Compositionality in Generative Adversarial Networks   Deep generative models seek to recover the process with which the observed
data was generated. They may be used to synthesize new samples or to
subsequently extract representations. Successful approaches in the domain of
images are driven by several core inductive biases. However, a bias to account
for the compositional way in which humans structure a visual scene in terms of
objects has frequently been overlooked. In this work, we investigate object
compositionality as an inductive bias for Generative Adversarial Networks
(GANs). We present a minimal modification of a standard generator to
incorporate this inductive bias and find that it reliably learns to generate
images as compositions of objects. Using this general design as a backbone, we
then propose two useful extensions to incorporate dependencies among objects
and background. We extensively evaluate our approach on several multi-object
image datasets and highlight the merits of incorporating structure for
representation learning purposes. In particular, we find that our structured
GANs are better at generating multi-object images that are more faithful to the
reference distribution. More so, we demonstrate how, by leveraging the
structure of the learned generative process, one can `invert' the learned
generative model to perform unsupervised instance segmentation. On the
challenging CLEVR dataset, it is shown how our approach is able to improve over
other recent purely unsupervised object-centric approaches to image generation.

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The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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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: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat