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US 12,585,949 B2Granted
System and method for designing efficient super resolution deep convolutional neural networks by cascade network training, cascade network trimming, and dilated convolutions
AssigneeSamsung Electronics Co., Ltd.
InventorsHaoyu Ren, Mostafa El-Khamy, Jungwon Lee
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Abstract
Apparatuses and methods of manufacturing same, systems, and methods are described. In one aspect, a method includes generating a convolutional neural network (CNN) by training a CNN having a plurality of convolutional layers, and performing cascade training on the trained CNN. The cascade training includes an iterative process of a plurality of stages, in which each stage includes inserting a residual block (ResBlock) and training the CNN with the inserted ResBlock.
Claims20
A method, comprising:
- generating a convolutional neural network (CNN), wherein generating the CNN comprises:
- training a CNN;
- and
- performing cascade training on the trained CNN,
- wherein cascade training comprises an iterative process of a plurality of stages, in which each stage comprises:
- inserting a residual block (ResBlock);
- and
- training the CNN with the inserted ResBlock.
5 more elements
An apparatus, comprising:
- one or more non-transitory computer-readable media;
- and
- at least one processor which, when executing instructions stored on the one or more non-transitory computer-readable media, performs the steps of:
- generating a convolutional neural network (CNN) by:
- training a CNN;
- and
- performing cascade training on the trained CNN,
- wherein cascade training comprises an iterative process of a plurality of stages, in which each of the stages comprises:
- inserting a residual block (ResBlock);
- and
- training the CNN with the inserted ResBlock.
8 more elements
Dependencies are inferred from the claim text.