backpropagation Luca Maria Gambardella Jürgen Schmidhuber max-pooling AlexNet Alex Krizhevsky Ilya Sutskever Geoffrey Hinton ImageNet competition Karen Simonyan Andrew Zisserman computer vision Hinton Schmidhuber machine learning speech recognition pattern recognition
Significant impacts in image or object recognition were felt from 2011 to 2012. Although CNNs trained by backpropagation had been around for decades, and GPU implementations of NNs for years, including CNNs, faster implementations of CNNs on GPUs were needed to progress on computer vision. In 2011, the DanNet by Dan Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, and Jürgen Schmidhuber achieved for the first time superhuman performance in a visual pattern recognition contest, outperforming traditional methods by a factor of 3. Also in 2011, DanNet won the ICDAR Chinese handwriting contest, and in May 2012, it won the ISBI image segmentation contest. Until 2011, CNNs did not play a major role at computer vision conferences, but in June 2012, a paper by Ciresan et al. at the leading conference CVPR showed how max-pooling CNNs on GPU can dramatically improve many vision benchmark records.  In September 2012, DanNet also won the ICPR contest on analysis of large medical images for cancer detection, and in the following year also the MICCAI Grand Challenge on the same topic. In October 2012, the similar AlexNet by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton won the large-scale ImageNet competition by a significant margin over shallow machine learning methods. 
The VGG-16 network by Karen Simonyan and Andrew Zisserman further reduced the error rate and
won the ImageNet 2014 competition, following a similar trend in large-scale speech recognition.
