The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Fait un tour historique du domaine: quels articles/travaux ont été marquants.

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Presentation transcript:

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) Fait un tour historique du domaine: quels articles/travaux ont été marquants et pourquoi. AlexNet (2012): gagnant de ImageNet avec 15% de ’top 5 error rate’, 2e=26% ZFNet (2013): ”was not only the winner of the competition in 2013 (13% error rate), but also provided great intuition as to the workings on CNNs and illustrated more ways to improve performance“ VGGNet (2014): 7% error rate. ”One of the most influential papers in my mind because it reinforced the notion that convolutional neural networks have to have a deep network of layers in order for this hierarchical representation of visual data to work. Keep it deep. Keep it simple.” GoogLeNet (2014): 6% error rate. “The authors showed that a creative structuring of layers (inception) can lead to improved performance and computational efficiency” ResNet (2015): 3.6% error rate. Aussi: Region-based CNNs, GANs, etc.