In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.
We investigate the problem of training neural networks from incomplete images without replacing missing values.
We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.
In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints.
In order to perform plausible interpolations in the latent space of a generative model, we need a measure that credibly reflects if a point in an interpolation is close to the data manifold being modelled, i. e. if it is convincing.
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.
In this paper, we focus on finding clusters in partially categorized data sets.