System and method for teaching compositionality to convolutional neural networks
AssigneeVicarious FPC, Inc.
InventorsAustin Charles Stone, Huayan Wang, D. Scott Phoenix, Dileep George
Life of this patent
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Abstract
A system for teaching compositionality to convolutional neural networks includes an unmasked convolutional neural network comprising a first set of convolutional neural network layers; a first masked convolutional neural network comprising a second set of convolutional neural network layers; the unmasked convolutional neural network and the first masked convolutional network sharing convolutional neural network weights; the system training the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.
Claims21
A system for teaching compositionality to convolutional neural networks, comprising:
- an unmasked convolutional neural network comprising a first set of convolutional neural network layers;
- wherein input to the unmasked convolutional neural network is maskless;
- a first masked convolutional neural network comprising a second set of convolutional neural network layers;
- wherein input to the first masked convolutional neural network is masked by a first object mask;
- wherein the unmasked convolutional neural network and the first masked convolutional network share convolutional neural network weights;
- wherein the first object mask suppresses activation outside of a region of a first object;
- wherein the system trains the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.
system of claim 1, wherein the unmasked and first masked convolutional neural network are trained to receive data feature input by setting activation of child feature nodes according to image features of an image;
- wherein the inferred output includes a classification of the image.
A method for teaching compositionality to convolutional neural networks, comprising:
- receiving an initial masked CNN structure, the structure comprising:
- an unmasked convolutional neural network comprising a first set of convolutional neural network layers;
- wherein input to the unmasked convolutional neural network is maskless;
- and
- a first masked convolutional neural network comprising a second set of convolutional neural network layers;
- wherein input to the first masked convolutional neural network is masked by a first object mask;
- wherein the unmasked convolutional neural network and the first masked convolutional network share convolutional neural network weights;
- wherein the first object mask suppresses activation outside of a region of a first object;
- receiving a training dataset;
- and
- learning the convolutional neural network weights;
- wherein learning the convolutional neural network weights comprises training the unmasked and first masked convolutional neural networks simultaneously on the training dataset based on an objective function that seeks to reduce both discriminative loss and compositional loss.
Dependencies are inferred from the claim text.