[cs702]

This can also be used for object recognition against invariant 3D representations, potentially with more accuracy than traditional convolutional neural net architectures.

Consider: their proof-of-concept face-recognition model achieves performance comparable to traditional convnets on faces with varying degree of pose, lighting, shape and texture, even though it was trained completely unsupervised. I would expect this type of model to beat the state of the art in face recognition and other similar tasks when fined-tuned with supervised training in the not-too-distant future.