AIMech AI
Home / AIO Classes / Network / Residual Neural Network

Residual Neural Network

Network AIO:ResidualNeuralNetwork NOT_REVIEWED
A deep neural network that employs skip connections to bypass layers facilitating learning of residual functions.
Ontology IRI: https://w3id.org/aio/ResidualNeuralNetwork

Ontology Structure & Relationships

Parent Classes
Branches
Network
Axiomatic Relationships

Annotations & Sources

Synonyms:
  • DRN exact
  • Deep Residual Network exact
  • ResNN exact
  • ResNet exact
Definition Sources:
Subsets:
  • https://w3id.org/aio/NetworkSubset

Curated AIMech Concepts Grounded to this Class

4 Concepts
Curated Concept Kind Mapping Relation Rationale
Residual Attention Network
AIMech:ResidualAttentionNetwork
 Architectures related_match The source uses residual building blocks and relates attention residual learning to ResNet. Its (1+mask)*trunk formulation is not itself an identity skip over the module input, and the framework permits different basic units, so the relationship is not equivalence.
ShakeDrop regularization
AIMech:ShakeDropRegularization
 Algorithms related_match ShakeDrop modifies training and inference on a residual host with a skip connection. The pinned AIO concept denotes a broader network architecture category, not this stochastic regularization procedure. The association does not assert equivalence, subclass membership or that every source configuration inherits all architectural components listed for the pinned target.
Shake-Shake regularization
AIMech:ShakeShakeRegularization
 Algorithms related_match The original procedure changes forward training, prescribed backward propagation and inference in a skip-connected host with two learned residual branches. This is a procedure-to-host association, not identity with the residual architecture, an is-a assertion or a requirement that every tested variant preserve every component of the pinned AIO class.
Stochastic depth
AIMech:StochasticDepth
 Algorithms related_match The original procedure trains a residual network by stochastically bypassing its residual transformations. The AIO target is the network architecture, while the native concept is a training/inference procedure.

Import Provenance & Upstream Synchronization

Source Repo: AIO GitHub
Release Version: aio.json
Commit: 397851c547…
License: CC-BY-4.0
Imported At: 2026-10-10T07:34:10Z
SHA256: 8f741d71408c…
Source File: data/aio/ResidualNeuralNetwork.yaml