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On Algorithmic Stability in Unsupervised Representation Learning. (arXiv:2106.05238v3 [cs.LG] UPDATED)
cs.CV updates on arXiv.org arxiv.org
In this paper, we investigate the algorithmic stability of unsupervised
representation learning with deep generative models, as a function of repeated
re-training on the same input data. Algorithms for learning low dimensional
linear representations -- for example principal components analysis (PCA), or
linear independent components analysis (ICA) -- come with guarantees that they
will always reveal the same latent representations (perhaps up to an arbitrary
rotation or permutation). Unfortunately, for non-linear representation
learning, such as in a variational auto-encoder (VAE) …
arxiv learning representation representation learning unsupervised