Creating deep learning models using feature augmentation
AssigneeInternational Business Machines Corporation
InventorPavel Kisilev
Life of this patent
Estimating the term…
Abstract
A computer implemented method of automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects using one or more hardware processors for executing a code. The code comprises code instructions for extracting a plurality of features from a plurality of sample media objects, generating a plurality of feature samples for each of the plurality of features by augmenting the plurality of features, training a classification function with the plurality of features samples and outputting the classification function for classifying one or more new media objects.
Claims20
A computer implemented method for reducing computation processing time, computation load and required storage space in media objects classification, by automatically creating a classification function trained with augmented representation of features extracted from a plurality of sample media objects, comprising;
- reducing computation processing time, computation load and required storage space in creating a classification function, of at least one hardware processor by causing said at least one hardware processor to execute a code comprising code instructions for performing the following:
- extracting a plurality of features from a plurality of sample media objects;
- upon completion of said extraction, generating a plurality of feature samples for each one of said plurality of features by augmenting said plurality of features;
- training said classification function with said plurality of features samples;
- and
- outputting said classification function for classifying at least one new media object.
A system for reducing computation processing time, computation load and required storage space in media objects classification, by creating a classification function trained with augmented representations of features extracted from a plurality of sample media objects, comprising:
- an interface module;
- a program store storing a code, wherein said code, when executed by a processor, causes said processor a reduction in computation processing time, computation load and required storage space in creating a classification function;
- and
- at least one processor coupled to said interface and said program store for executing said stored code, said code comprising:
- code instructions to extract a plurality of features from a plurality of sample media objects;
- code instructions to generate, upon completion of said extraction, a plurality of features samples for each one of said plurality of features by augmenting said plurality of features;
- code instructions to train said classification function with said plurality of features samples;
- and
- code instructions to output said classification function for classifying at least one new media object.
A computer program product for reducing computation processing time, computation load and required storage space in media objects classification, by creating a classification function trained with augmented representations of features extracted from a plurality of sample media objects, comprising:
- a non-transitory computer readable storage medium;
- first program instructions to extract a plurality of features from a plurality of sample media objects;
- second program instructions to generate a plurality of features samples for each one of said plurality of features by augmenting said plurality of features;
- third program instructions to train a classification function with said plurality of features samples;
- and
- fourth program instructions to output said classification function for classifying at least one new media object;
- wherein said first, second, third and fourth program instructions are executed by at least one processor from said non-transitory computer readable storage medium and wherein said execution of said first, second, third and fourth program instructions causes a reduction in computation processing time, computation load and required storage space in creating said classification function.
Dependencies are inferred from the claim text.