Toyota Jidosha Kabushiki Kaisha

Katholieke Universiteit Leuven

Eth Zurich the Swiss Federal Institute of Technology Zurich

Simon Vandenhende

Gabriel Othmezouri

Luc Van Gool

Marc Proesmans

Wim Abbeloos

Stamatios Georgoulis

Wouter Van Gansbeke

A computer-implemented method for training a classifier (Φη), including: training a pretext model (ΦΘ) to learn a pretext task, so as to minimize a distance between an output of a source sample via the pretext model (ΦΘ) and an output of a corresponding transformed sample via the pretext model (ΦΘ), the transformed sample being a sample obtained by applying a transformation (T) to the source sample; S) determining a neighborhood (NXi) of samples (Xi) of a dataset (SD) in the embedding space; S) training the classifier (Φη) to predict respective estimated probabilities Φηj(Xi), j=1 . . . C, for a sample (Xi) to belong to respective clusters (Cj), by using a second training criterion which tends to: maximize a likelihood for a sample and its neighbors (Xj) of its neighborhood (Nxi) to belong to the same cluster; and force the samples to be distributed over several clusters.

Computer-implemented training method, classification method and system and computer-readable recording medium

Growing community of inventors

Leuven, Belgium

Simon Vandenhende