This is related to one shot meta learning, so I have sample and query in meta train set, and sample and query in meta test set, the task is to make neural network classify by showing only one image, that is, show it one image in the sample of meta test set, and it would give correct classification for query in meta test set, for this we show images to neural network in sample of meta train set, and ask it to predict correctly in the query of meta train set, if it gives incorrect prediction, then loss increases, task is to minimize loss, once we have a neural network that gives low loss in meta train set, then it would give correct result by showing only one image in sample of meta test set and make correct prediction for query of meta test set. What I think is that we want our neural network to learn a whole hierarchy during meta training stage, that is, face, lips, tongue, eyes…, and after meta training we have a neural network that has learnt hierarchy, so in meta test set, it would adapt quickly to the sample, and be able to predict query of meta test set correctly.