Samsung Electronics Co., Ltd.

Minkyoung Cho

Seungwon Lee

Songyi Han

Wonjo Lee

Youngmin Oh

Donghyuk Kwon

Won-jo Lee

A neural network pruning method includes acquiring a first task accuracy of an inference task processed by a pretrained neural network, pruning, based on a channel unit, the neural network by adjusting weights between nodes of channels based on a preset learning weight and based on a channel-by-channel pruning parameter corresponding to a channel of each of a plurality of layers of the pretrained neural network, updating the learning weight based on the first task accuracy and a task accuracy of the pruned neural network, updating the channel-by-channel pruning parameter based on the updated learning weight and the task accuracy of the pruned neural network, and repruning, based on the channel unit, the pruned neural network based on the updated learning weight and based on the updated channel-by-channel pruning parameter.

Method and apparatus with neural network pruning

An Image processing method and apparatus are provided. The Image processing method includes obtaining a kernel that is deterministically defined based on an extension ratio of a first feature map, up-sampling the first feature map to a second feature map by performing a transposed convolution operation between the first feature map and the kernel, and outputting the second feature map.

Method and apparatus with image processing

A method and apparatus for the pruning of a neural network is provided. The method sets a weight threshold value based on a weight distribution of layers included in a neural network, predicts a change of inference accuracy of a neural network by pruning of each layer based on the weight threshold value, determines a current subject layer to be pruned with a weight threshold value among the layers included in the neural network, and prunes a determined current subject layer.

Neural network method and apparatus

A processor-implemented method of performing a convolution operation is provided. The method includes obtaining input feature map data and kernel data, determine the kernel data based on a number of input channels of the input feature map, a number of output channels of an output feature map, and a number of groups of the input feature map data and a number of groups of the kernel data related to the convolution operation, and performing the convolution operation based on the input feature map data and the determined kernel data.

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Incheon, South Korea

Minkyoung Cho