I present my investigations into explicitly mapping dependencies between image areas and identify limitations of that approach. I continue with an analysis of variational autoencoders. Building on the insight, I train VAEs on different sized image patches extracted from the CIFAR-10 and CelebA datasets. The patch VAEs are used to compute a similarity metric between individual patches. As an evaluation, linear models are trained to perdict the labels given the unsupervised representations. The patch-VAEs allow to obtain better classification accuracies then using the representations of a global VAE on CIFAR-10 and CelebA datasets. The patch VAEs are used to compute a similarity metric between individual patches. As an evalutation, linear models are trained to predict the labels given  the unsupervised representations. The patch-VAEs allow to obtain better classifications accuracies  then using the representations of a global VAE on CIFAR-10 and CelebA. I find that for VAEs the features  of the earlier layers give a better linear classification performance than their representations.