Modular Neural Network

A Modular Neural Network is a component-based hierarchical artificial neural network that can support modular learning tasks through neural module composition.

AKA: Modular Neural-Network, Modular NN, Multi-Module Neural Network.
Context:
- It can typically enable Modular Function Learning through specialized neural modules that solve sub-tasks independently.
- It can typically support Modular Knowledge Representation by decomposing complex problems into modular components.
- It can typically facilitate Modular Transfer Learning through reusable neural modules across different task domains.
- It can typically implement Modular Architecture Patterns such as parallel module processing and sequential module chaining.
- It can typically provide Modular Scalability by adding or removing neural modules without retraining the entire modular network.
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- It can often incorporate Modular Attention Mechanisms to coordinate information flow between neural modules.
- It can often utilize Modular Gating Systems to control module activation based on input characteristics.
- It can often support Modular Specialization where each neural module becomes an expert module for specific input patterns.
- It can often enable Modular Interpretability through isolated module analysis of individual neural components.
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- It can range from being a Simple Modular Neural Network to being a Complex Modular Neural Network, depending on its modular architecture complexity.
- It can range from being a Homogeneous Modular Neural Network to being a Heterogeneous Modular Neural Network, depending on its modular component diversity.
- It can range from being a Static Modular Neural Network to being a Dynamic Modular Neural Network, depending on its modular configuration flexibility.
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- It can integrate with Modular Training Frameworks for distributed module training.
- It can interface with Modular Optimization Systems for module-specific parameter tuning.
- It can connect to Modular Evaluation Frameworks for component-wise performance assessment.
- It can communicate with Modular Deployment Systems for selective module deployment.
- It can synchronize with Modular Version Control Systems for module lifecycle management.
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Example(s):
- Adversarial Modular Architectures, such as:
  - Generative Adversarial Network (GAN) demonstrating adversarial modular learning through generator-discriminator modules.
  - Variational Autoencoder GAN (VAE-GAN) demonstrating hybrid modular generation through encoder-decoder-discriminator modules.
  - CycleGAN demonstrating bidirectional modular transformation through dual generator-discriminator pairs.
- Hierarchical Modular Architectures, such as:
  - FeUdal Network (FuN) demonstrating hierarchical modular reinforcement learning through manager-worker modules.
  - Hierarchical Temporal Memory (HTM) Network demonstrating cortical modular processing through columnar neural modules.
  - Progressive Neural Network demonstrating lateral modular connections through column-based neural modules.
- Compositional Modular Architectures, such as:
  - Encoder-Decoder Neural Network demonstrating sequential modular transformation through encoding-decoding modules.
  - Pointer-Generator Network demonstrating hybrid modular generation through attention-copying modules.
  - Neural Module Network (NMN) demonstrating dynamic modular composition through question-specific module assembly.
- Ensemble Modular Architectures, such as:
  - Mixture of Experts (MoE) Network demonstrating gated modular specialization through expert neural modules.
  - Capsule Network demonstrating hierarchical modular representation through capsule modules.
  - Routing Network demonstrating adaptive modular pathways through routing modules.
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Counter-Example(s):
- Memory Augmented Neural Network, which extends neural networks with external memory rather than modular components.
- Bidirectional Neural Network, which processes information flow in multiple directions within a single architecture rather than through distinct modules.
- Monolithic Neural Network, which implements all functionality in a single integrated architecture without modular separation.
- End-to-End Neural Network, which learns direct mappings without intermediate modular representations.
See: Neural Network Architecture, Component-Based Machine Learning, Hierarchical Neural Network, Multi-Task Learning System, Transfer Learning System, Neural Architecture Search.

References

2020

(TensorFlow, 2020) ⇒ https://www.tensorflow.org/tutorials/generative/dcgan Retrieved:2020-11-29.
- QUOTE: Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator ("the artist") learns to create images that look real, while a discriminator ("the art critic") learns to tell real images apart from fakes.
  During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at telling them apart. The process reaches equilibrium when the discriminator can no longer distinguish real images from fakes.