Convolutional Rank Filters in Deep Learning
Deep neural nets mainly rely on convolutions to generate feature maps and transposed convolutions to create images. Rank filters are already critical components of neural nets under the disguise of max-pooling, rank-pooling, and max-Unpooling layers. We propose a framework that generalizes them, and...
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| Format: | Others |
| Language: | en |
| Published: |
2020
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| Online Access: | http://hdl.handle.net/10393/41120 http://dx.doi.org/10.20381/ruor-25344 |