System and method for an adjustable neural network

A system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
• measuring feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features;
• and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain;
• measuring octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features;
• measuring a pattern of octaves learned in the CNN, resulting in a measurement pattern;
• comparing that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN;
• and
• modifying the CNN based on the level of adaptability of the CNN, resulting in a modified CNN, wherein the modifying of the CNN further comprises:
• identifying at least one layer of the CNN corresponding to the at least one drop-off point;
• identifying, within a database of neural network layers, at least one replacement layer associated which resonates with the pre-defined features;
• and
• replacing the at least one layer of the CNN with the at least one replacement layer, resulting in the modified CNN.

A non-transitory computer-readable storage medium having instructions stored which, when executed by a computer system, cause the computer system to perform operations comprising:

• measuring feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features;
• and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain;
• measuring octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features;
• measuring a pattern of octaves learned in the CNN, resulting in a measurement pattern;
• comparing that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN;
• and
• modifying the CNN based on the level of adaptability of the CNN, resulting in a modified CNN, wherein the modifying of the CNN further comprises:
• identifying at least one layer of the CNN corresponding to the at least one drop-off point;
• identifying, within a database of neural network layers, at least one replacement layer associated which resonates with the pre-defined features;
• and
• replacing the at least one layer of the CNN with the at least one replacement layer, resulting in the modified CNN.

A method comprising:

• measuring, via a processor, feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features;
• and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain;
• measuring, via the processor, octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features;
• measuring a pattern of octaves learned in the CNN, resulting in a measurement pattern;
• comparing that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN;
• modifying the CNN based on the level of adaptability of the CNN, resulting in a modified CNN;
• measuring, via the processor, applicability of a new image with the modified CNN;
• determining, via the processor based on the applicability meeting a predefined threshold, that the new image represents a new category, resulting in a determination;
• generating, based on the determination, a new branch of features associated with the new image;
• and
• adding the new branch of features to the modified CNN, resulting in an updated, modified CNN.

A system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
• measuring feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features;
• and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain;
• measuring octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features;
• measuring a pattern of octaves learned in the CNN, resulting in a measurement pattern;
• comparing that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN;
• modifying the CNN based on the level of adaptability of the CNN, resulting in a modified CNN;
• measuring applicability of a new image with the modified CNN;
• determining, based on the applicability meeting a predefined threshold, that the new image represents a new category, resulting in a determination;
• generating, based on the determination, a new branch of features associated with the new image;
• and
• adding the new branch of features to the modified CNN, resulting in an updated, modified CNN.

A non-transitory computer-readable storage medium having instructions stored which, when executed by a computer system, cause the computer system to perform operations comprising:

• measuring feature applicability for an octave of a Convolutional Neural Network (CNN) at a standard scale, resulting in (1) at least one drop-off point where the octave no longer resonates with pre-defined features;
• and (2) a common drop-off between the CNN and at least one other CNN trained on at least one other separate domain;
• measuring octave resonance for a plurality of CNNs trained on large data sets with a distribution of octaves for features;
• measuring a pattern of octaves learned in the CNN, resulting in a measurement pattern;
• comparing that measurement pattern to the pre-defined features, resulting in a level of adaptability of the CNN;
• modifying the CNN based on the level of adaptability of the CNN, resulting in a modified CNN;
• measuring applicability of a new image with the modified CNN;
• determining, based on the applicability meeting a predefined threshold, that the new image represents a new category, resulting in a determination;
• generating, based on the determination, a new branch of features associated with the new image;
• and
• adding the new branch of features to the modified CNN, resulting in an updated, modified CNN.