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,...
Główni autorzy: | Lenc, K, Vedaldi, A |
---|---|
Format: | Conference item |
Wydane: |
IEEE
2015
|
Podobne zapisy
-
Understanding Image Representations by Measuring Their Equivariance and Equivalence
od: Lenc, K, i wsp.
Wydane: (2018) -
Unsupervised learning of object frames by dense equivariant image labelling
od: Thewlis, J, i wsp.
Wydane: (2017) -
Equivariant quantum cohomology and the geometric Satake equivalence
od: Viscardi, Michael
Wydane: (2016) -
Learning equivariant structured output SVM regressors
od: Vedaldi, A, i wsp.
Wydane: (2012) -
Induction equivalence for equivariant D-modules on rigid analytic spaces
od: Ardakov, K
Wydane: (2023)