Deep-learning

When starting with Deep Learning on your own (without any legacy code or compatibility constraint), it may be daunting to choose one among the many frameworks available.

[Update 22.03.2018: link to correct Youtube publication.]

U-Net was proposed in 2015 for medical image segmentation. You can find the original paper, along with some video introduction on the project homepage. Its structure is relatively simple and shallow, so it seems to be well fitted for a learning work.

It turns out I completed my PhD in 2012 just before Deep Learning started to boom and be the Next Big Thing in Computer Vision. The various pieces were already there (neural networks, back propagation, large scale databases, GPGPU…), but still, I somehow managed to slip through it. Later at work, I’ve used standard Pattern Recognition approaches for realtime applications, and later the more complex and efficient Gradient Boosted Trees, but still, no luck at trying Deep Learning.