[visionlist] Participation in the free International AI Doctoral Academy (AIDA) Short course "Domain Adaptation and Generalization" by Prof. Vittorio Murino and Dott. Pietro Morerio, April 08, 2022

[Ioanna Koroni]

COURSE TITLE: Domain Adaptation & Generalization


LECTURER:       Vittorio Murino,  <mailto:vittorio.murino at univr.it>
vittorio.murino at univr.it; Pietro Morerio,  <mailto:pietro.morerio at iit.it>
pietro.morerio at iit.it


ORGANIZER:     University of Verona and Istituto Italiano di Tecnologia


CONTENT AND ORGANIZATION:            A standard assumption of learning based
models is that training and test data share the same input distribution.
However, models trained on given datasets perform poorly when tested on data
acquired in different settings. This problem is known as domain shift and is
particularly relevant, e.g., for visual models of agents acting in the real
world or when we have no labeled data available for our target scenario. In
the latter case, for instance, we could use synthetically generated data to
obtain data for our target task, but this would create a mismatch between
training (synthetic) and test (real) images. Filling the gap between these
two different input distributions is the goal of domain adaptation (DA)
algorithms. In particular, the goal of DA is to produce a model for a target
domain (for which we have few or no labeled data) by exploiting labeled data
available in a different, source, domain. Various DA techniques have been
developed to address the domain shift problem. In this short course, we will
provide an introduction to these algorithms and to domain adaptation and
generalization. In particular, we will first introduce the domain shift
problem, showing application scenarios where it is strongly present. Second,
we will provide an overview of the algorithms that have been developed to
tackle this issue. In particular, we will focus on the last research trends
addressing the DA problem within deep neural networks. Lastly, we will
address the domain generalization problem, which is a more challenging task
because it assumes that target data is also not available, implying that the
training algorithm should be devised to generalize as much as possible
without any adaptation to the target in order to properly classify never
observed, out-of-distribution samples.


 

REGISTRATION: Free of charge

 

WHEN: April 8, 2022 - 14.00-18.00 CEST

 

WHERE: Online (link to be provided by the Lecturer after
registration/enrollment)

 

HOW TO REGISTER and ENROLL: 

Both AIDA and non-AIDA students are encouraged to participate in this short
course. 

 

If you are an AIDA Student* already, please: 

Step (a): Register in the course by sending an email to pietro.morerio[at]
<http://iit.it/> iit.it for your registration. 

AND 

Step (b): Enroll in the same course in the AIDA system using the "Enroll on
this Course" button, which you can find here
<https://www.i-aida.org/course/domain-adaptation-generalization/> , so that
this course enters your AIDA Certificate of Course Attendance. 

 

If you are not an AIDA Student do only step (a). 

 

*The International AI Doctoral Academy (AIDA) has 73 members, which are top
AI Universities, Research centers and Industries:
<https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20220227155943016697
9&URLID=3&ESV=10.0.15.7233&IV=BB037ED3B12F2777180E2FE00438846D&TT=1645977584
923&ESN=%2FwqscGludfJ5ZK96xMM5VPA%2FydclXWMakKctvxs0k%2FE%3D&KV=153696172928
0&B64_ENCODED_URL=aHR0cHM6Ly93d3cuaS1haWRhLm9yZy8&HK=26993ACB83D95EAF8317C1C
7BF9186A3E3AA1B22217DD0908BCE3AA33B4A2627> https://www.i-aida.org/

AIDA Students should have been registered in the AIDA system already (they
are PhD students or PostDocs that belong only to the AIDA Members listed in
this page:
<https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20220227155943016697
9&URLID=1&ESV=10.0.15.7233&IV=B2A1D7FA8A1918941E8C560177C33C06&TT=1645977584
923&ESN=i%2B%2BnSdQCGpTb9fthJLBydzqhw49U25GdbhRwiH%2Fbj%2B0%3D&KV=1536961729
280&B64_ENCODED_URL=aHR0cHM6Ly93d3cuaS1haWRhLm9yZy9hYm91dC9tZW1iZXJzLw&HK=96
B88288F267D1C8CD7204AD7A248EF36C1112EDC1123E6DC0BCD76D646BE1AA> Members)



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