A DYNAMIC COMPACT DEEP LEARNING ARCHITECTURE FOR PATTERN RECOGNITION

Dynamic deep learning networks are a class of neural network architectures that are able to adapt their structure and parameters during training or inference. This allows the network to better handle changing input data, improve performance on tasks with non-stationary data distributions, and learn more efficiently by only using the necessary number of neurons and connections. Examples of dynamic deep learning networks include networks that incorporate attention mechanisms, architectures that use gating mechanisms to control the flow of information, and networks that can add
or prune neurons and connections during training. These architectures have been applied to a variety of pattern recognition tasks such as image classification, natural
language processing and speech recognition. With the ability to adapt to the changing input data and optimize the network structure, dynamic deep learning networks have the potential to improve the performance of neural networks and make them more practical for real-world applications.