Understanding image representations by measuring their equivariance and equivalence
Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate three key mathematical properties of representations: equivariance,...
المؤلفون الرئيسيون: | Lenc, K, Vedaldi, A |
---|---|
التنسيق: | Conference item |
منشور في: |
IEEE
2015
|
مواد مشابهة
-
Understanding Image Representations by Measuring Their Equivariance and Equivalence
حسب: Lenc, K, وآخرون
منشور في: (2018) -
Unsupervised learning of object frames by dense equivariant image labelling
حسب: Thewlis, J, وآخرون
منشور في: (2017) -
Equivariant quantum cohomology and the geometric Satake equivalence
حسب: Viscardi, Michael
منشور في: (2016) -
Learning equivariant structured output SVM regressors
حسب: Vedaldi, A, وآخرون
منشور في: (2012) -
Induction equivalence for equivariant D-modules on rigid analytic spaces
حسب: Ardakov, K
منشور في: (2023)