I know that Deep CNN's is all everyone cares about today, and there are a lot of papers about state of the art CNN's for image classification; Alexnet, (Googles) Inception, Microsofts ResNet etc.
However, I do not feel these networks is the right answer to most peoples problems. While most of the big CNN's focuses on classifying any image from a giant pool of labels - I fell most real world problems want to classify an image from a much more limited set of labels.
Say I want to classify images of animals: My input is probably images of animals, but instead of having 10.000 labels I may have somewhere between 50 and 200. I can of course just use one of the large generic networks, but my intuition you should be able to gain some performance (in model size, memory footprint, training time, and/or error rate) by using a smaller network and/or a network optimized for fewer labels.
Is there any research into this? Anything like the ImageNet classification challenge, but with the focus on a much more limited scope?