ImageNet classification with Deep Convolutional Neural Networks – [paper explained]

Last updated on January 12th, 2016

Here are some slides I made on the very interesting work by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton titled, “ImageNet Classification with Deep Convolutional Neural Networks”.

convThis was really the first time I took a deep 🙂 look at Convolutional Neural Networks (CNNs). I was so blown away by their performance that I have been exploring CNNs ever since then.

High-level summary:
Basically, in 2012, Krizhevsky et al. won a competition to classify 1000 different classes across thousands of images. They won by a pretty substantial margin. After this, most (if not all) of the top competing approaches now rely on CNNs to extract strong image features.

This was the first paper I read/presented on the topics of CNNs, so I’m particularly fond of this work. If you find this topic interesting, check out these slides that describe a CNN-based approach to win two medical image analysis competitions.

Hopefully these slides help convey the key ideas from their work.