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,...
Päätekijät: | Lenc, K, Vedaldi, A |
---|---|
Aineistotyyppi: | Conference item |
Julkaistu: |
IEEE
2015
|
Samankaltaisia teoksia
-
Understanding Image Representations by Measuring Their Equivariance and Equivalence
Tekijä: Lenc, K, et al.
Julkaistu: (2018) -
Unsupervised learning of object frames by dense equivariant image labelling
Tekijä: Thewlis, J, et al.
Julkaistu: (2017) -
Equivariant quantum cohomology and the geometric Satake equivalence
Tekijä: Viscardi, Michael
Julkaistu: (2016) -
Learning equivariant structured output SVM regressors
Tekijä: Vedaldi, A, et al.
Julkaistu: (2012) -
Induction equivalence for equivariant D-modules on rigid analytic spaces
Tekijä: Ardakov, K
Julkaistu: (2023)