In this work, we propose modeling the proximal operators of unrolled neural networks with scale-equivariant convolutional neural networks in order to improve the data-efficiency and robustness to drifts in scale of the images that might stem from the variability of patient anatomies or change in ...
In this work, we pay attention to scale changes, which regularly appear in various tasks due to the changing distances between the objects and the camera. First, we introduce the general theory for building scale-equivariant convolutional networks with steerable filters. We develop scale-convolution...
4) location equivariant 位置同变 1. Under the asymmetric loss,the best location equivariant and best location-scale equivariant predictors are given for solving the practical economic problem. 在非对称Linex损失下,对具体的经济问题,给出了最优位置同变和最优位置尺度同变预测量。
Nerfstudio Implementation of RENI++: A Rotation-Equivariant, Scale-Invariant, Natural Illumination Prior - JADGardner/ns_reni
In this work, we present Allegro, an equivariant deep-learning approach that retains the high accuracy of the recently proposed class of equivariant MPNNs15,26,27,37,39,40while combining it with strict locality and thus the ability to scale to large systems. We demonstrate that Allegro not onl...
Article SIMULTANEOUS BEST EQUIVARIANT ESTIMATION IN LOCATION-SCALE FAMILIES was published on January 1, 2001 in the journal Statistics & Risk Modeling (volume 19, issue 1).
However, no method has focused on the difference in scale within an image caused by a 3-dimensional to 2-dimensional projection. We proposed a new scale-equivariant convolution method that focuses on the relationship between the object distance and scale ratio in the image.Marumo, Hidetaka...
Scale-Equivariant Steerable NetworksIvan SosnovikMicha SzmajaArnold SmeuldersInternational Conference on Learning Representations
In both classification and segmentation tasks, the scale-equivariant architectures improve dramatically the generalization to unseen scales compared to a convolutional baseline. Besides, in our experiments morphological scale-spaces outperformed the Gaussian scale-space in geometrical tasks....
(60), further wherein the scale-equivariant convolutional neural network (60) comprises a convolutional layer, wherein the convolutional layer is configured to provide a convolution output based on a plurality of steerable filters of the convolutional layer and a convolution input, wherein the ...