Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

1. Kevin McGuinness
kevin.mcguinness@dcu.ie
Assistant Professor
School of Electronic Engineering
Dublin City University
http://bit.ly/dlcv2018
#DLUPC

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Zeiler and Fergus. Visualizing and understanding convolutional networks ECCV 2014

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Zeiler and Fergus. Visualizing and understanding convolutional networks ECCV 2014

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Zeiler and Fergus. Visualizing and understanding convolutional networks ECCV 2014

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Zeiler and Fergus. Visualizing and understanding convolutional networks ECCV 2014

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Zeiler and Fergus. Visualizing and understanding convolutional networks ECCV 2014

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22. He et al. Deep Residual Learning for Image Recognition CVPR 2016
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Bold curves: validation error
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He et al. Deep Residual Learning for Image Recognition CVPR 2016

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He et al. Deep Residual Learning for Image Recognition CVPR 2016

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He et al. Deep Residual Learning for Image Recognition CVPR 2016

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COCO
Common Objects in Context

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