Cross-domain Variational Capsules for Information Extraction
- URL: http://arxiv.org/abs/2210.09053v1
- Date: Thu, 13 Oct 2022 20:04:36 GMT
- Title: Cross-domain Variational Capsules for Information Extraction
- Authors: Akash Nagaraj, Akhil K, Akshay Venkatesh, Srikanth HR
- Abstract summary: The intent was to identify prominent characteristics in data and use this identification mechanism to auto-generate insight from data in other unseen domains.
An information extraction algorithm is proposed which is a combination of Variational Autoencoders (VAEs) and Capsule Networks.
Noticing a dearth in the number of datasets that contain visible characteristics in images belonging to various domains, the Multi-domain Image Characteristics dataset was created and made publicly available.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we present a characteristic extraction algorithm and the
Multi-domain Image Characteristics Dataset of characteristic-tagged images to
simulate the way a human brain classifies cross-domain information and
generates insight. The intent was to identify prominent characteristics in data
and use this identification mechanism to auto-generate insight from data in
other unseen domains. An information extraction algorithm is proposed which is
a combination of Variational Autoencoders (VAEs) and Capsule Networks. Capsule
Networks are used to decompose images into their individual features and VAEs
are used to explore variations on these decomposed features. Thus, making the
model robust in recognizing characteristics from variations of the data. A
noteworthy point is that the algorithm uses efficient hierarchical decoding of
data which helps in richer output interpretation. Noticing a dearth in the
number of datasets that contain visible characteristics in images belonging to
various domains, the Multi-domain Image Characteristics Dataset was created and
made publicly available. It consists of thousands of images across three
domains. This dataset was created with the intent of introducing a new
benchmark for fine-grained characteristic recognition tasks in the future.
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