Deep Residual Learning in Image Classification and Transfer Learning

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"The Beauty of Artificial Intelligence Seminar Series"

Date:           Monday, 25 April 2016
Time:           10:00am - 12 noon
Venue:          Lecture Theater G (near lifts 25/26), HKUST

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(Seminar III)
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Speaker:        Shaoqing REN
                University of Science and Technology of China

Title:          "Deep Residual Learning in Image Classification and
                 Transfer Learning"

Time:           11:10am to 11:50am

Abstract:

Deeper neural networks are difficult to train. We'll present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. In this framework, we explicitly
reformulate the layers as learning residual functions with reference to
the layer inputs, instead of learning unreferenced functions. We can
provide comprehensive empirical evidence showing that these residual
networks are easier to optimize, and therefore gain more accuracy from
considerably increased depth. With this deep network, our team won the 1st
place in the ILSVRC 2015 (aka, ImageNet Competition) classification task
and detection task, COCO 2015 detection task and segmentation task. In
this talk, I will introduce our method and findings of deep residual
learning in image classification and transfer learning.

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Biography:

Shaoqing Ren is currently a final year Ph.D. student in the joint Ph.D.
program between the University of Science and Technology of China and
Microsoft Research Asia.  His supervisor is Dr. Jian Sun. His research
interest includes computer vision and machine learning, especially
detection and localization of face and general objects.