Deep Convolutional Neural Networks
The goal of this post is to serve as a nice introduction to deep architectures before diving to read the original publications where they are described.
I feel there is a lack of help in the research community. A little bit of the time of one researcher by making nice visualizations, dashboards, demos or even videos could save the time of all the researchers coming after him/her, and innovation will grow faster.
My contribution is by giving intuition in understanding the evolution of the so used deep convolutional neural networks as the default option for computer vision problems.
INDEX
0.2: 1x1 Convolution
(1): LeNet — LeCun 1998 — Paper ………………………….(TBI)
(2) AlexNet — Krizhevsky 2012 — Paper
(3): GoogLeNet / Inception — Szegedy 2014 — Paper…….(TBI)
(4): VGG — Simonyan / Zisserman 2014 — Paper…………(TBI)
(5): ResNets for ImageNet — He 2015 — Paper
- DenseNets for CIFAR10 — Huang 2016 — Paper
(7): FractalNets — Larsson — 2016 — Paper………………..(TBI)
(8): SENets — Hu — 2018 — Paper
(9): MobileNets — Howard — 2016 — Paper……………….(TBI)