Deep Convolutional Network
Network AIO:DeepConvolutionalNetwork NOT_REVIEWED
A deep neural network specialized for analyzing visual imagery using shared-weight architecture and translation-equivariant feature maps.
Ontology IRI:
https://w3id.org/aio/DeepConvolutionalNetwork
Ontology Structure & Relationships
Parent Classes
Branches
Axiomatic Relationships
-
BFO:0000051 →
AIO:ConvolutionalLayer(Convolutional Layer) -
BFO:0000051 →
AIO:HiddenLayer(Hidden Layer) -
BFO:0000051 →
AIO:InputLayer(Input Layer) -
BFO:0000051 →
AIO:KernelLayer(Kernel Layer) -
BFO:0000051 →
AIO:OutputLayer(Output Layer) -
BFO:0000051 →
AIO:PoolingLayer(Pooling Layer)
Annotations & Sources
Synonyms:
- CNN exact
- ConvNet exact
- Convolutional Neural Network exact
- DCN exact
Definition Sources:
Subsets:
https://w3id.org/aio/NetworkSubset
Curated AIMech Concepts Grounded to this Class
3 Concepts| Curated Concept | Kind | Mapping Relation | Rationale |
|---|---|---|---|
Perspective Transformer Nets
AIMech:PerspectiveTransformerNets |
Architectures | related_match | The original host uses a convolutional image encoder and an up-convolutional volume decoder, supporting a qualified architectural association with the pinned visual-network class. That class does not identify the supplied-camera projection, silhouette objective or full PTN framework. Its translation-equivariant feature-map description is not asserted for the whole model through the fully connected bottleneck. This relation is neither equivalence nor subclass membership, and the name does not imply an attention-based Transformer architecture. |
Residual Attention Network
AIMech:ResidualAttentionNetwork |
Architectures | broad_match | The source explicitly constructs a deep convolutional image-recognition network, which fits this broader architecture category. Its learned multiscale attention modules provide the narrower identity. |
SimNet
AIMech:SimNet |
Architectures | related_match | The family generalizes convolutional constructions using similarity and MEX operations. Generic members need not use the pinned convolutional components or image-specific scope, so the relationship is recorded without equivalence or a forced subclass direction. |
Import Provenance & Upstream Synchronization
Source Repo: AIO GitHub
Release Version:
aio.jsonCommit:
397851c547…License: CC-BY-4.0
Imported At: 2026-10-10T07:34:10Z
SHA256:
8f741d71408c…
Source File:
data/aio/DeepConvolutionalNetwork.yaml