International Business Machines Corporation

Ralf Kaestner

Alexander Velizhev

Peter Willem Jan Staar

Martin M Rufli

Michele Dolfi

Christoph Auer

Konstantinos Bekas

Leonidas Georgopoulos

Dal Noguer Hidalgo

Rita Kuznetsova

Elliot Jacques Vincent

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

A computer-implemented method, computer program product, and computer system are provided. The method comprises training a machine-learning model using an initial set of training data samples, receiving a new training data sample, and predicting a label for the new training data sample. The method also comprises, upon determining that a prediction quality value for the predicted label of the new training data sample is below a predefined quality value, adding the new training data sample to the initial set, thereby building an extended training data set. The method also comprises retraining the machine-learning model using the extended training data set.

Iterative training of a machine learning model

Computer-implemented methods and apparatus are provided for annotating digital images of line plots with ground truth labels. For each digital image, such a method includes supplying image data defining the image of a line plot to a machine-learning model trained to generate a set of control points defining a spline corresponding to the line plot. The method further comprises displaying the spline, and the set of control points, superimposed on the image in a graphical user interface and, in response to user manipulation via the graphical user interface of one or more control points, dynamically adjusting the displayed spline in accordance with manipulated control points whereby the displayed spline can be adjusted for conformity with the line plot. The set of control points for the adjusted spline is then stored as a ground truth label for the image.

Annotation of digital images for machine learning

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Othmarsingen, Switzerland

Ralf Kaestner