The key word here is recognition. Better, faster, more efficient classifiers.

The algorithms for training a better representation of the features, not just features itself is a big deal, but it is, again, supervised learning.

The adversary approach, which gives additional feedback for better training, is mere sophisticated supervised learning.

Take one of the best papers survey (which is an emergent new genre in hipster's blogs) and one will see that there is nothing fundamentally new, apart from sophistication and additional feedback.

In fact, there is a discussion about the definition of unsupervised learning, because of the emergence of techniques like word2vec, where it essentially supervised learning but construct all its tasks from the data itself without any labelling.

CNNs, RNNs, LSTMs, reinforcement learning, gradient descent, backpropagation - all that was used in the 90s, but given the limited computing power they had, people simply couldn't run large enough models on large enough data to get results.