System and method for identifying and mitigating ambiguous data in machine learning architectures
AssigneeSingulos Research Inc.
InventorsBradley Quinton, Trent McClements, Michael Lee, Scott Chin
Life of this patent
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Abstract
Machine learning systems are valuable for processing data in many scenarios including understanding objects and the environment in mixed reality systems. The present disclosure provides ambiguity-aware machine learning methods and systems that are capable of identifying input data that will potentially lead to erroneous predictions arising from training data ambiguity; capable of learning to identify training data as ambiguous during the training process; and, capable of adjusting the training process to account for training data that is Ambiguous.
Claims52
A method of generating prediction output in a machine learning system having a machine learning engine and a prediction engine, the method comprising:
- receiving a data input at the machine learning engine;
- generating, by the machine learning engine, an output associated with the data input based on a set of internal parameters and transmitting the output to the prediction engine;
- determining, at the prediction engine, a label from a set of possible labels and an ambiguous indication based on the machine learning engine output and a prediction function;
- generating a prediction output that indicates the determined label and the determined ambiguous indication.
A Machine learning system comprising:
- a machine learning engine configured to:
- receive a data input;
- and
- generate an output associated with the data input based on a set of internal parameters and transmitting the output to the prediction engine;
- and
- a prediction engine configured to:
- determine a label from a set of possible labels and an ambiguous indication based the machine learning engine output and a prediction function;
- and
- generate a prediction output that indicates the determined label and the determined ambiguous indication.
An ambiguity-aware machine learning system for identifying an object, comprising:
- a user device having a sensor for acquiring information indicative of the object, the user device communicatively coupled to a processor configured by machine-readable instructions to:
- generate image data based on the information acquired by the sensor, the image data indicative of a first perspective of the object;
- generate, using a machine learning engine, an output based on applying a set of machine learning parameters associated with the machine learning engine to the image data;
- generate, using a prediction engine, an output label and an ambiguous indication based on the machine learning engine output and a prediction function associated with the prediction engine, and
- generate a prediction output that includes the output label and the ambiguous indication corresponding to the object.
A method for training a machine learning system to identify and mitigate ambiguity, the method comprising:
- training a machine learning engine using a training set comprising a plurality of input data associated with a corresponding plurality of known labels, wherein a subset of the plurality of input data is further associated with a corresponding ambiguity label;
- generating, during training of the machine learning engine, for each of the plurality of input data in the training set, a first machine learning output indicative of a potential label associated with an input data and a second machine learning output indicative of a potential ambiguity associated with the input data;
- generating, using a cost function, a cost output for each of the plurality of input data based on the first machine learning output and the second machine learning output;
- adjusting a set of parameters associated with the machine learning engine based on the cost function, wherein the set of associated parameters condition the behaviour of the machine learning engine.
Dependencies are inferred from the claim text.