Imagine a scenario where we give you only 10 images of a person and ask you to identify whether a new image is of the same person. As humans, we can classify such tasks with ease. However, the deep learning-based algorithms that we have learned so far would require hundreds/ thousands of labeled examples to classify accurately.
Multiple algorithms that fall in the meta-learning paradigm come in handy to solve this scenario. In this section, we will learn about Siamese networks, prototypical networks, and relation matching networks that work towards solving the few-images problem.
All three algorithms aim towards learning to compare two images to come up with a score for how similar the images are.
Here's an example of what to expect during few-shot classification:
In the preceding representative datasets, we have shown a few images of each class to the network while training and asked it to predict the class for a new image based on the images.
So...
