Date: 14 October 2020
Time: 12:00pm – 1:15pm
Institution: Institute of Data Science, NUS

The Brown Bag Workshops from Oct to Dec 2020 is part of a 10-part series created in collaboration with the 6 x Local Universities (NUS, NTU, SMU, SUTD, SIT & SUSS) & 1 x Institute of Higher Learning (NP) and the main focus is to examine how Digitalisation will impact the Future of Work and how COVID has accelerated some of these changes.

The first 4 learn over lunch sessions are brought to you by the Lifelong Learning Institute and will be broadcasted LIVE from their venue virtually, for the safety and wellbeing of all participants, in light of the current COVID-19 situation.

In this Brown Bag session (14 Oct), HeadHunt held the 2nd learn over lunch talk with our speaker, Associate Professor Ji Hui, who shared on the topic of Deep Learning for Photo Restoration.

Session – Deep Learning for Photo Restoration

Associate Professor Ji Hui
Associate Professor at Department of Mathematics
Faculty Affiliate at Institute of Data Science

In recent years, deep learning has emerged as one powerful tool in image processing and computer vision, including image recovery. Image recovery refers to how to recover a high-quality image from a degraded image, a fundamental low-level task with a wide range of applications. Most existing works are based on supervised learning which calls an external image dataset to train a recovery network. In many real scenarios, the instruction of an unbiased and comprehensive image dataset can be a challenging and sometimes impossible task, e.g., medical and scientific imaging. Contradict to popular belief, we will show in this talk that, without seeing any other image data, a deep network still can learn how to recover an image with solid performance. Specifically, this talk will present  a self-supervised image recovery network with state-of-the-art performance, which is built on two concepts: (1) Bernoulli-sampling-based data augmentation and (2) dropout-based training/testing scheme.

About the Speaker:
Click here to find out more.

Missed our session? Watch the full Brown Bag session below:

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