Adaptive Neural Layer for Globally Filtered Segmentation

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Summary

This study aspired to invent an adaptive global frequency-filtering neural layer to "learn" optimal frequency filter for each image together with the weights of the segmentation network itself, and boosted typical U-Net segmentation performance by 10% and made the training of other popular models almost twice faster.

Type
article
Published
2020-10-02
Cited by
1
References
41
Access
Open access

Keywords

Computer science, Artificial intelligence, Segmentation, Filter (signal processing), Convolutional neural network

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