Article ; Online: FP-nets as novel deep networks inspired by vision.
2022 Volume 22, Issue 1, Page(s) 8
Abstract: Feature-product networks (FP-nets) are inspired by end-stopped cortical cells with FP-units that multiply the outputs of two filters. We enhance state-of-the-art deep networks, such as the ResNet and MobileNet, with FP-units and show that the resulting ... ...
| Abstract | Feature-product networks (FP-nets) are inspired by end-stopped cortical cells with FP-units that multiply the outputs of two filters. We enhance state-of-the-art deep networks, such as the ResNet and MobileNet, with FP-units and show that the resulting FP-nets perform better on the Cifar-10 and ImageNet benchmarks. Moreover, we analyze the hyperselectivity of the FP-net model neurons and show that this property makes FP-nets less sensitive to adversarial attacks and JPEG artifacts. We then show that the learned model neurons are end-stopped to different degrees and that they provide sparse representations with an entropy that decreases with hyperselectivity. |
|---|---|
| MeSH term(s) | Artifacts ; Deep Learning ; Learning ; Neurons ; Vision, Ocular |
| Language | English |
| Publishing date | 2022-01-01 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2106064-2 |
| ISSN | 1534-7362 ; 1534-7362 |
| ISSN (online) | 1534-7362 |
| ISSN | 1534-7362 |
| DOI | 10.1167/jov.22.1.8 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.