Scale Equivariant U-Net

TL;DR

This paper introduces SEU-Net, a scale-equivariant U-Net architecture that improves generalization in semantic segmentation tasks across different scales by explicitly modeling scale transformations and incorporating scale-dropout.

Contribution

The paper proposes a novel scale-equivariant U-Net architecture with explicit equivariance to scales and translations, and introduces scale-dropout to enhance scale generalization.

Findings

SEU-Net significantly outperforms standard U-Net in scale generalization.

Scale-dropout improves generalization in pet segmentation.

Explicit scale-equivariance benefits semantic segmentation tasks.

Abstract

In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equivariant networks are suited to computer vision tasks where the same classes of objects appear at different scales, like in most semantic segmentation tasks. Recently, convolutional layers equivariant to a semigroup of scalings and translations have been proposed. However, the equivariance of subsampling and upsampling has never been explicitly studied even though they are necessary building blocks in some segmentation architectures. The U-Net is a representative example of such architectures, which includes the basic elements used for state-of-the-art semantic segmentation. Therefore, this paper introduces the Scale Equivariant U-Net (SEU-Net), a U-Net that is made approximately equivariant to a semigroup of scales and translations through careful application of subsampling and upsampling layers and the use of aforementioned scale-equivariant layers. Moreover, a scale-dropout is proposed in order to improve generalization to different scales in approximately scale-equivariant architectures. The proposed SEU-Net is trained for semantic segmentation of the Oxford Pet IIIT and the DIC-C2DH-HeLa dataset for cell segmentation. The generalization metric to unseen scales is dramatically improved in comparison to the U-Net, even when the U-Net is trained with scale jittering, and to a scale-equivariant architecture that does not perform upsampling operators inside the equivariant pipeline. The scale-dropout induces better generalization on the scale-equivariant models in the Pet experiment, but not on the cell segmentation experiment.