Electronic device and method for training or applying neural network model
AssigneeCoretronic Corporation
InventorsYi-Fan Liou, Yen-Chun Huang
Life of this patent
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Abstract
An electronic device and a method for training or applying a neural network model are provided. The method includes the following steps. An input data is received. Convolution is performed on the input data to generate a high-frequency feature map and a low-frequency feature map. One of upsampling and downsampling is performed to match a first size of the high-frequency feature map and a second size of the low-frequency feature map. The high-frequency feature map and the low-frequency feature map are concatenated to generate a concatenated data. The concatenated data is inputted to an output layer of the neural network model.
Claims14
An electronic device for training or applying a neural network model, comprising:
- a transceiver;
- a storage medium configured to store multiple modules and the neural network model;
- and
- a processor configured to couple to the storage medium and the transceiver, and configure to access and execute the modules, wherein the modules comprise:
- a data collection module configured to receive an input data via the transceiver;
- and
- a calculation module configured to perform convolution on the input data to generate a high-frequency feature map and a low-frequency feature map, and perform one of upsampling and downsampling to match a first size of the high-frequency feature map and a second size of the low-frequency feature map, concatenate the high-frequency feature map and the low-frequency feature map to generate a concatenated data, and input the concatenated data to an output layer of the neural network model.
A method for training or applying a neural network model, comprising:
- receiving an input data;
- performing convolution on the input data to generate a high-frequency feature map and a low-frequency feature map;
- performing one of upsampling and downsampling to match a first size of the high-frequency feature map and a second size of the low-frequency feature map;
- concatenating the high-frequency feature map and the low-frequency feature map to generate a concatenated data;
- and
- inputting the concatenated data to an output layer of the neural network model.
Dependencies are inferred from the claim text.