This is really clever. So basically iiuc, they set up a network to encode down to a representation that consists of parameters for a rendering engine. In order to ensure that this is the representation that is learned, the decoding stage is used to re-render the image subject to transformations and perform the decoding based on a an initial reduction phase after rendering. I.e. it is like an autoencoder, but the inner-most reduced representation is forced to be related to a graphics rendering engine by manipulating related transformation parameters.
Not only is this interesting from the point of view of using it for learning how to generate images, but it is a novel way to force a semantic internal representation instead of leaving it up to a regularisation strategy and interpreting the sparse encoding post-hoc. It forces the internal representation to be inherently "tweakable."
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.
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This is really clever. So basically iiuc, they set up a network to encode down to a representation that consists of parameters for a rendering engine. In order to ensure that this is the representation that is learned, the decoding stage is used to re-render the image subject to transformations and perform the decoding based on a an initial reduction phase after rendering. I.e. it is like an autoencoder, but the inner-most reduced representation is forced to be related to a graphics rendering engine by manipulating related transformation parameters.
Not only is this interesting from the point of view of using it for learning how to generate images, but it is a novel way to force a semantic internal representation instead of leaving it up to a regularisation strategy and interpreting the sparse encoding post-hoc. It forces the internal representation to be inherently "tweakable."
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.