About the Tutorial

In this tutorial we would like to start with an introduction to the major deep learning architecture such as FC, CNN and RNN. Following this, the primary content of the workshop will include various contemporary applications using versions of Siamese, Autoencoders and Generative Adversarial Networks, which are built upon the basic architectures. We shall cover a selection of works from other research groups and that from our group. There is a specially designed hand-on sessions worksheet (given to each participant) that can suitably augment the tutorial learning outcome.



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Dr. Aditya Nigam

Indian Institute of Technology Mandi

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Dr. Arnav Bhavsar

Indian Institute of Technology Mandi

Potential Target Audience

Since the conference itself is in the area of computer vision, pattern analysis and its applications, the target audience would be similar to that of the conference. The expected background only consists of basics of machine learning and computer vision with some linear algebra and probability. For the hands on session, anyone comfortable with Python will be able to participate and benefit from it.

Turorial Motivation

Considering the current popularity and interest in deep learning, the tutorial which focuses on some of its contemporary aspects and applications, is expected to be of interest to a large fraction of the CAIP audience. We believe that the tutorial aligns well with the theme of the conference which focuses on computer vision and its applications. Lecture are planned so that there are enough details in the topics so as to provide important insights.