>> I don't get why deep learning researchers are so hung up on learning everything from scratch. It is easier to formulate the problem when you are dealing wit

>> I don't get why deep learning researchers are so hung up on learning everything from scratch. It is easier to formulate the problem when you are dealing with only one specific task. Transfer learning is useful, but in its current format it is limited, and can only borrow features from tasks that are similar. Meta-learning is trying to tackle this problem specifically, promising result is presented, but still long way to go to be useful in real-world production. In order to NOT learning from scratch, we need understand the representation of knowledge. For neural networks, the knowledge is parameters that learnt. However, the parameter is tied to the task that your trained your network with, so we are back to square one. I doubt continuous differentiable tensors are the ONLY representation of human's knowledge. It might represent the hard-to-explain intuition part of it, however, a large, probably larger part of our knowledge is better represented by category/hierarchy/graph/rules that is not easily differentiable, which current deep learning techniques struggle to model. So, IMO, it is not really that researchers are obsessed with supervised learning, it is probably that they have no better alternatives.