Abstract | ||
---|---|---|
We introduce Logarithm-Networks (Log-Nets), a novel bio-inspired type of network architecture based on logarithms of feature maps followed by convolutions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1007/978-3-030-61616-8_7 | ICANN (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Philipp Grüning | 1 | 0 | 0.68 |
Thomas Martinetz | 2 | 65 | 6.13 |
Erhardt Barth | 3 | 653 | 58.33 |