Deep learning has garnered much attention and hype over the years. It is no surprise that a ton of research work is being shared in reputed competitions, conferences, and journals worldwide centered around deep learning. It is particularly the image classification architectures that have been enjoying the spotlight for some years now, with iterative improvements being shared on a regular basis. Let us have a quick look at some of the best-performing and popular state-of-the-art deep image classification architectures:
- AlexNet: This is the network that can be credited for opening the floodgates. Designed by one of the pioneers of deep learning, Geoffrey Hinton and team, this network reduced the top-five error rate to just 15.3%. It was also one of the first architectures to leverage GPUs for speeding up the learning process.
- VGG...
