International Business Machines Corporation

Dal Noguer Hidalgo

Konstantinos Bekas

Peter Willem Jan Staar

Michele Dolfi

Christoph Auer

Leonidas Georgopoulos

Ralf Kaestner

Alexander Velizhev

Rita Kuznetsova

A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Efficient ground truth annotation

Growing community of inventors

Zurich, Switzerland

Dal Noguer Hidalgo