Stacked Capsule Autoencoders
akosiorek.github.io
akosiorek.github.io
1. Attention mechanism (transformers). Really moved forward NLP. It allows long distance dependencies without the need for sequential processing (like in RNNs). Generally scales much better (see BERT/GPT-2/XL-Net/etc)
2. Separable convolutions. Significantly sped-up the standard CV networks. (mobile net, but now everything, really).
Hinton's "What is wrong with convolution neural nets?" is still true to the state of the art in convnets. Gradually turning features into objects like we do in deep learning is limiting. Distinguishing feature integration from object detection like humans do and how to do it is an open question. It can take another 10-15 years.
Why do some Chinese focus on SNNs? I suspect they just have a different worldview because they are exposed to a different information stream in their language. But SNNs are still a gross approximation. Also, why assume it needs to look like an NN at all?