Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Canonical Correlation Analysis   Canonical Correlation Analysis (CCA) is a statistical technique used to
extract common information from multiple data sources or views. It has been
used in various representation learning problems, such as dimensionality
reduction, word embedding, and clustering. Recent work has given CCA
probabilistic footing in a deep learning context and uses a variational lower
bound for the data log likelihood to estimate model parameters. Alternatively,
adversarial techniques have arisen in recent years as a powerful alternative to
variational Bayesian methods in autoencoders. In this work, we explore
straightforward adversarial alternatives to recent work in Deep Variational CCA
(VCCA and VCCA-Private) we call ACCA and ACCA-Private and show how these
approaches offer a stronger and more flexible way to match the approximate
posteriors coming from encoders to much larger classes of priors than the VCCA
and VCCA-Private models. This allows new priors for what constitutes a good
representation, such as disentangling underlying factors of variation, to be
more directly pursued. We offer further analysis on the multi-level
disentangling properties of VCCA-Private and ACCA-Private through the use of a
newly designed dataset we call Tangled MNIST. We also design a validation
criteria for these models that is theoretically grounded, task-agnostic, and
works well in practice. Lastly, we fill a minor research gap by deriving an
additional variational lower bound for VCCA that allows the representation to
use view-specific information from both input views.

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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat