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

June 16, 2022, 1:11 a.m. | Pola Schwöbel, Frederik Warburg, Martin Jørgensen, Kristoffer H. Madsen, Søren Hauberg

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv lg networks transformer

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