[visionlist] 3rd IEEE BDTL joint with IEEE BIgData 2018 Workshop

Allan Ding allanzmding at gmail.com
Tue Oct 16 13:26:11 -05 2018

Please help announce our workshop.


Although widely applied on considerable scientific research, conventional
statistical machine learning revolves on a simplified assumption that the
training data, from which the algorithms learn, are drawn i.i.d. from the
same distribution as the test data, to which the learned models are
applied. This assumption, being broken down by numerous real-world
applications nowadays, especially with the emergence of large-scale data
from the private internal data, or the public Internet, has fundamentally
restricted the development of practical learning algorithms. More examples
include but are not limited to: (1) speech recognition when speakers have
strong dialects from different countries, regions, genders, or aging
groups; (2) surveillance system where captured suspects' faces are
side-view, under unknown lighting conditions, in low-resolutions, different
from the conditions of the registered faces in the system.

On the other hand, multi-view data generated from various viewpoints or
multiple sensors are commonly seen in real-world applications. For example,
the popular commercial depth sensor Kinect uses both visible light and
near-infrared sensors for depth estimation; autopilot uses both visual and
radar sensors to produce real-time 3D information on the road; face
analysis algorithms prefer face images from different views for high
fidelity reconstruction and recognition. However, such data with large view
divergence would result in an enormous challenge: data lying in different
views show a large divergence preventing them from a fair comparison. In
general, different views can be treated as different domains drawn from
different distributions. Therefore, there is an urgent need to mitigate the
view divergence by either fusing the knowledge across multiple views or
adapting knowledge from some views to others.

The key problems discussed above come down to two popular research topics
in modern data mining: transfer learning and multi-view learning. While the
first problem emphasizes exploring sample-wise correspondence across
different views, the second problem focuses more on the generic knowledge
transfer or adaptation, e.g., intelligent recognition. Essentially, they
both attempt to address the issues of knowledge transfer or fusion between
different domains, which makes significant sense given a large amount of
available auxiliary data from other datasets, sensors, or modalities.

Best regards,

Dr. and Assistant Professor
Department of Computer, Information and Technology
Indiana University-Purdue University Indianapolis
799 West Michigan St., ET 301N,
Indianapolis IN 46202
Phone: +1-(317)274-9705
E-mail: z <allanzmding at gmail.com>d2 at iu.edu
Homepage: *http://allanding.net/ <http://allanding.net/>*
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