NeurIPS2019 paper, looks interesting:

https://arxiv.org/abs/1904.00243

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Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries.

I’m not familiar with this line of research, but it seems like this could have significant implications on how models are trained, as many current benchmark datasets are static. I’d be interested in hearing thoughts from those more familiar with the method.