We will go through several model definitions that have achieved state-of-the-art results in the ImageNet competitions. We will look at them individually on the following topics.
AlexNet is the first publication that started a wide interest in deep learning for computer vision. Krizhevsky et al. (https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) proposed AlexNet and it has been a pioneer and influential in this field. This model won the ImageNet 2013 challenge. The error rate was 15.4%, which was significantly better than the next. The model was relatively a simple architecture with five convolution layers. The challenge was to classify 1,000 categories of objects. The image and data had 15 million annotated images with over 22,000 categories. Out of them, only a 1,000 categories are used for the competition. AlexNet used ReLU as the activation function and found it was training several times...
