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Polar Transformer Networks

· 2017 · cs.CV · arXiv 1709.01889

2 Pith papers cite this work. Polarity classification is still indexing.

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abstract

Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations. The result is a network invariant to translation and equivariant to both rotation and scale. PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier. PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling. The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.

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cs.CV 2

years

2024 1 2019 1

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UNVERDICTED 2

representative citing papers

Learning Color Equivariant Representations

cs.CV · 2024-06-13 · unverdicted · novelty 6.0

Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.

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Showing 2 of 2 citing papers.

  • SurReal: Fr\'echet Mean and Distance Transform for Complex-Valued Deep Learning cs.CV · 2019-06-24 · unverdicted · none · ref 10 · internal anchor

    SurReal architecture applies weighted Fréchet mean convolution and distance-based FC layers to complex data, improving accuracy on MSTAR (94% to 98%) and RadioML with 8-10% of baseline model size.

  • Learning Color Equivariant Representations cs.CV · 2024-06-13 · unverdicted · none · ref 19 · internal anchor

    Presents hue-, saturation-, luminance-equivariant GCNNs via a direct-image lifting layer that resolves invalid RGB issues in prior CEConv work and reports better OOD generalization plus sample efficiency.