Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminative Multimodal Learning via Conditional Priors in Generative
  Models   Deep generative models with latent variables have been used lately to learn
joint representations and generative processes from multi-modal data. These two
learning mechanisms can, however, conflict with each other and representations
can fail to embed information on the data modalities. This research studies the
realistic scenario in which all modalities and class labels are available for
model training, but where some modalities and labels required for downstream
tasks are missing. We show, in this scenario, that the variational lower bound
limits mutual information between joint representations and missing modalities.
We, to counteract these problems, introduce a novel conditional multi-modal
discriminative model that uses an informative prior distribution and optimizes
a likelihood-free objective function that maximizes mutual information between
joint representations and missing modalities. Extensive experimentation shows
the benefits of the model we propose, the empirical results showing that our
model achieves state-of-the-art results in representative problems such as
downstream classification, acoustic inversion and annotation generation.

36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ