Computer Vision and the Deep Learning Transformation

Since 2012, with the introduction of deep and large convolutional neural networks trained on large datasets, the research field of computer vision has been transformed and dominated by the renaissance of neural networks in their new guise as efficient trainable high capacity function approximators. The latter seems to be true with the crucial caveat that you have to have sufficient labelled training data. However, even in these data-rich training regime the traits that led to the original disillusionment with neural networks in the 90's - dark arts needed to successfully train and their black box nature - still persist to some degree. In this talk I will give an overview of the exciting results and tweaks on familiar ideas within neural networks that have brought so much success to computer vision and then present current work on trying to "understand" what a network can and does learn.