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Bibliografie

Conference Paper (international conference)

H-NeXt: The next step towards roto-translation invariant networks

Karella Tomáš, Šroubek Filip, Blažek Jan, Flusser Jan, Košík Václav

: 34th British Machine Vision Conference 2023, p. 1-14

: British Machine Vision Conference 2023 /34./, (Aberdeen, GB, 20231120)

: GA21-03921S, GA ČR

: H-NeXT, robustness to unseen deformations, parameter-efficient roto-translation invariant network, classification on unaugmented training set

: http://library.utia.cas.cz/separaty/2023/ZOI/karella-0578508.pdf

(eng): The widespread popularity of equivariant networks underscores the significance of parameter efficient models and effective use of training data. At a time when robustness to unseen deformations is becoming increasingly important, we present H-NeXt, which bridges the gap between equivariance and invariance. H-NeXt is a parameter-efficient roto-translation invariant network that is trained without a single augmented image in the training set. Our network comprises three components: an equivariant backbone for learning roto-translation independent features, an invariant pooling layer for discarding roto-translation information, and a classification layer. H-NeXt outperforms the state of the art in classification on unaugmented training sets and augmented test sets of MNIST and CIFAR-10

: JD

: 20206