View source for US Patent Application 18309335. TRAINING NEURAL NETWORKS WITH A LESSER REQUIREMENT FOR LABELLED TRAINING DATA simplified abstract

=TRAINING NEURAL NETWORKS WITH A LESSER REQUIREMENT FOR LABELLED TRAINING DATA=


==Organization Name==

[[Robert Bosch GmbH]]


==Inventor(s)==

[[Piyapat Saranrittichai of Nuernberg (DE)]]

[[Andres Mauricio Munoz Delgado of Schoenaich (DE)]]

[[Chaithanya Kumar Mummadi of Pittsburgh PA (US)]]

[[Claudia Blaiotta of Stuttgart (DE)]]

[[Volker Fischer of Renningen (DE)]]

==TRAINING NEURAL NETWORKS WITH A LESSER REQUIREMENT FOR LABELLED TRAINING DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18309335 titled 'TRAINING NEURAL NETWORKS WITH A LESSER REQUIREMENT FOR LABELLED TRAINING DATA

==Simplified Explanation==

The patent application describes a method for training a neural network to determine a task output based on a given task.
* The method involves providing training records of measurement data, which can be either unlabeled or labeled.
* The encoder network is trained to map these training records to representations that fulfill a self-consistency condition or correspond to ground truth.
* Task training records that are labeled with ground truth are also provided.
* The association network and task head networks are trained to map the representations obtained from the encoder network to task outputs that correspond to the ground truth.
* The training process is guided by a task loss function, which measures the accuracy of the obtained task outputs compared to the labeled ground truth.


==Original Abstract Submitted==

A method for training a neural network for determining a task output with respect to a given task. The method includes: providing unlabeled and/or labelled encoder training records of measurement data; training the encoder network to map encoder training records to representations towards the goal that these representations, and/or or one or more work products derived from the representations, fulfil a self-consistency condition or correspond to ground truth; providing task training records that are labelled with ground truth; and training the association network and the task head networks towards the goal that, when a task training record is mapped to a representation using the encoder network, and the representation is mapped to a task output by the combination of the association network and the task head networks, the so-obtained task output corresponds to the ground truth with which the training record is labelled, as measured by a task loss function.