Canon Kabushiki Kaisha

Hongxing Gao

Tsewei Chen

Dongchao Wen

Wei Tao

Junjie Liu

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and a storage medium. In a forward propagation of the multi-layer neural network model, the number of input feature maps is expanded and a data computation is performed by using the expanded input feature maps.

Training and application method of a multi-layer neural network model, apparatus and storage medium

The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and storage medium. A number of channels of a filter in at least one convolutional layer in the multi-layer neural network model is expanded, and a convolution computation is performed by using the filter after expanding the number of channels, so that the performance of the network model does not degrade while simplifying the network model.

A method and an apparatus for optimizing and applying a multilayer neural network model, and a storage medium are provided. The optimization method includes, dividing out at least one sub-structure from the multilayer neural network model to be optimized, wherein a tail layer of the divided sub-structure is a quantization layer, and transferring operation parameters in layers other than the quantization layer to the quantization layer for each of the divided sub-structures and updating quantization threshold parameters in the quantization layer based on the transferred operation parameters. When a multilayer neural network model optimized based on the optimization method is operated, the necessary processor resources can be reduced.

Method and apparatus for optimizing and applying multilayer neural network model, and storage medium

A training and application method for a neural network model is provided. The training method determines the first network model to be trained and sets a downscaling layer for at least one layer in the first network model, wherein the number of filters and filter kernel of the downscaling layer are identical to those of layers to be trained in the second network model. Filter parameters of the downscaling layer are transmitted to the second network model as training information. By this training method, training can also be performed even when the scale of the layer for training in the first network model is different from that of the layers to be trained in the second network model, and the amount of lost data is small.

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Beijing, China

Hongxing Gao