A computer-implemented method, the method comprising: - providing an autoencoder; - providing a dataset of images, each image including a respective object representation, wherein the object representations are each a representation of an instance of a same class of objects; and - learning the autoencoder (g W' ∘ f W ) based on the dataset, the learning including minimization of a reconstruction loss (E(W,W')), the reconstruction loss including a term ∑ i = 1 n inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i , ∑ i = 1 n inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i + ρ h , ∑ i = 1 n inf h ∈ G d ( g W ' ∘ f W x i ̅ , h ∘ x i )) that penalizes for each respective image ( x i ) a distance ( d q (g W' ∘ f W x i , x ι ̅ ) = inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i ) between the result ( g W' ∘ f W ( x i )) of applying the autoencoder to the respective image ( x i ) and the set of results ( x̅ l ) of applying at least part of a group ( ) of transformations (h) to the object representation of the respective image ( x i ), wherein the autoencoder is invariant with respect to at least part of the group of transformations, wherein the autoencoder includes a transformation component configured to apply each one of the at least part of the group of transformations to the object representation of an input image, an aggregation component configured to be applied to results stemming from the transformation component, an encoding component (fw) and a decoding component ( g W' ); and - using the autoencoder for performing non-linear interpolation on images of objects of the class, for denoising, the autoencoder being a denoising autoencoder, or for compressing data into a compact representation. - providing an autoencoder; - providing a dataset of images, each image including a respective object representation, wherein the object representations are each a representation of an instance of a same class of objects; and - learning the autoencoder (g W' ∘ f W ) based on the dataset, the learning including minimization of a reconstruction loss (E(W,W')), the reconstruction loss including a term ∑ i = 1 n inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i , ∑ i = 1 n inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i + ρ h , ∑ i = 1 n inf h ∈ G d ( g W ' ∘ f W x i ̅ , h ∘ x i )) that penalizes for each respective image ( x i ) a distance ( d q (g W' ∘ f W x i , x ι ̅ ) = inf h ∈ G d g W ′ ∘ f W x i , h ∘ x i ) between the result ( g W' ∘ f W ( x i )) of applying the autoencoder to the respective image ( x i ) and the set of results ( x̅ l ) of applying at least part of a group ( ) of transformations (h) to the object representation of the respective image ( x i ), wherein the autoencoder is invariant with respect to at least part of the group of transformations, wherein the autoencoder includes a transformation component configured to apply each one of the at least part of the group of transformations to the object representation of an input image, an aggregation component configured to be applied to results stemming from the transformation component, an encoding component (fw) and a decoding component ( g W' ); and - using the autoencoder for performing non-linear interpolation on images of objects of the class, for denoising, the autoencoder being a denoising autoencoder, or for compressing data into a compact representation. <EOS>