منابع مشابه
Deep Rotation Equivariant Network
Recently, learning equivariant representations has attracted considerable research attention. Dieleman et al. introduce four operations which can be inserted into convolutional neural network to learn deep representations equivariant to rotation. However, feature maps should be copied and rotated four times in each layer in their approach, which causes much running time and memory overhead. In ...
متن کاملRotation Equivariant Minkowski Valuations
The projection body operator Π, which associates with every convex body in Euclidean space Rn its projection body, is a continuous valuation, it is invariant under translations and equivariant under rotations. It is also well known that Π maps the set of polytopes in Rn into itself. We show that Π is the only non-trivial operator with these properties. MSC 2000: 52B45, 52A20
متن کاملLearning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model’s prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equi...
متن کاملOptimally Rotation-Equivariant Directional Derivative Kernels
We describe a framework for the design of directional derivative kernels for two-dimensional discrete signals in which we optimize a measure of rotation-equivariance in the Fourier domain. The formulation is applicable to rst-order and higher-order derivatives. We design a set of compact, separable, linear-phase derivative kernels of di erent orders and demonstrate their accuracy.
متن کاملDeep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using rando...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2018
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.02.029