Dynamic generation of data sets for training machine-trained network

A non-transitory machine readable medium storing a program which when executed by at least one processing unit dynamically generates training data sets for training a machine-trained (MT) network, the training data sets comprising input data sets and expected output data sets, the program comprising sets of instructions for:

• generating a first group of training data sets for training the MT network;
• propagating each generated training data set in the first group of training data sets through the MT network to produce an output data set, each output data set used to train the MT network based on a loss function that comprises a measurement of a difference between the produced output data set and its expected output data set;
• determining whether further training of the MT network is required based on the measurements of the differences between the produced output data sets and their expected output data sets, said determining comprising identifying a subset of input data sets for which the MT network produced worse results than for other subsets of input data sets;
• after determining that further training of the MT network is required, generating an additional group of training data sets related to the identified subset of input data sets for further training of the MT network;
• and
• propagating the training data sets in the additional group through the MT network to train the MT network in order to address deficiency in the training of the MT network based on the identified subset of input data sets,
• wherein the sets of instructions for determining, generating, and propagating are executed iteratively until at least one of a set of stopping conditions has been met.

A non-transitory machine readable medium storing a program which when executed by at least one processing unit dynamically generates training data sets for training a machine-trained (MT) network, the training data sets comprising input data sets and expected output data sets, the program comprising sets of instructions for:

• generating a first group of training data sets for training the MT network;
• propagating each generated training data set in the first group of training data sets through the MT network to produce an output data set, each output data set used to train the MT network based on a loss function that comprises a measurement of a difference between the produced output data set and its expected output data set;
• determining whether further training of the MT network is required based on the measurements of the differences between the produced output data sets and their expected output data sets, said determining comprising identifying a subset of input data sets for which the MT network produced worse results than for other subsets of input data sets;
• after determining that further training of the MT network is required, generating a second group of training data sets related to the identified subset of input data sets for further training of the MT network, wherein the second group of training data sets is generated based on a group of training data sets for which the measurements of the differences between the produced output data sets and their expected output data sets are large relative to other groups of training data sets;
• and
• propagating the training data sets in the second group through the MT network to train the MT network in order to address deficiency in the training of the MT network based on the identified subset of input data sets.

A non-transitory machine readable medium storing a program which when executed by at least one processing unit dynamically generates training data sets for training a machine-trained (MT) network, the training data sets comprising input data sets and expected output data sets, the program comprising sets of instructions for:

• generating a first group of training data sets for training the MT network, wherein:
• the input data set of a particular training data set comprises a set of computer-generated images representing a same computer-generated scene, each image corresponding to an image captured by an image capture device in a virtual image capture device array placed in the computer-generated scene, each image representing the same computer-generated scene at the same time;
• and
• the expected output data set of the particular training data set comprises a set of computer-generated values based on a position of the virtual image capture device array and objects in the computer-generated scene;
• propagating each generated training data set in the first group of training data sets through the MT network to produce an output data set, each output data set used to train the MT network based on a loss function that comprises a measurement of a difference between the produced output data set and its expected output data set;
• determining whether further training of the MT network is required based on the measurements of the differences between the produced output data sets and their expected output data sets, said determining comprising identifying a subset of input data sets for which the MT network produced worse results than for other subsets of input data sets;
• after determining that further training of the MT network is required, generating a second group of training data sets related to the identified subset of input data sets for further training of the MT network;
• and
• propagating the training data sets in the second group through the MT network to train the MT network in order to address deficiency in the training of the MT network based on the identified subset of input data sets.