Probabilistic Spatial Transformer Networks. (arXiv:2004.03637v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2004.03637

Spatial Transformer Networks (STNs) estimate image transformations that can
improve downstream tasks by `zooming in' on relevant regions in an image.
However, STNs are hard to train and sensitive to mis-predictions of
transformations. To circumvent these limitations, we propose a probabilistic
extension that estimates a stochastic transformation rather than a
deterministic one. Marginalizing transformations allows us to consider each
image at multiple poses, which makes the localization task easier and the
training more robust. As an additional benefit, the stochastic …

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