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US 11,699,070 B2Granted
Method and apparatus for providing rotational invariant neural networks
AssigneeSamsung Electronics Co., Ltd.
InventorsMostafa El-Khamy, Jungwon Lee, Yoo Jin Choi, Haoyu Ren
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Abstract
A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.
Claims18
A method, comprising:
- receiving a first input of an image in a first orientation;
- and
- training a kernel to be symmetric, thereby causing an output corresponding to the first input to be identical to an output corresponding to a second input of the image in a second orientation, and
- training a convolutional neural network to be symmetric by training multiple kernels at different layers to be symmetric,
- wherein the second orientation is a flipped version of the image with respect to the first orientation of the image.
2 more elements
A system, comprising:
- a memory;
- and
- a processor configured to:
- receive a first input of an image in a first orientation;
- and
- train a kernel to be symmetric, thereby causing an output corresponding to the first input to be identical to an output corresponding to a second input of the image in a second orientation, and
- train a convolutional neural network to be symmetric by training multiple kernels at different layers to be symmetric,
- wherein the second orientation is a flipped version of the image with respect to the first orientation of the image.
5 more elements
A method, comprising:
- receiving, by a neural network, a first input of an image in a first orientation;
- generating a first loss function based on a first output associated with the first input;
- receiving, by the neural network, a second input of the image in a second orientation;
- generating a second loss function based on a second output associated with the second input;
- training the neural network to minimize a sum of the first loss function and the second loss function;
- and
- training a convolutional neural network to be symmetric by training multiple kernels at different layers to be symmetric,
- wherein the second orientation is a flipped version of the image with respect to the first orientation of the image.
5 more elements
Dependencies are inferred from the claim text.