Dynamic Neural Networks in Deep Learning｜Applications, Flexibility, and Design
2025-07-08 13:45:29 11 Report
Log in to view full content
This mind map provides an insightful overview of 'Dynamic Neural Network,' a concept crucial in advancing modern artificial intelligence. It explores the dynamic architecture of neural networks, including techniques like cascading, early exiting, layer skipping, and dynamic pruning, which enhance flexibility and efficiency. The map also delves into dynamic parameters, focusing on convolution kernels, linear transformations, and activation functions. Furthermore, it highlights dynamic input processing for both image and sequence inputs. Training methodologies such as reinforcement learning and knowledge distillation are discussed, alongside applications in computer vision and natural language processing. Lastly, it addresses challenges like storage demands and stability.
Other works by the author
Outline/Content
Application
CV
Image Segmention
Object detection
Image synthesis
Video recognition
Action detection and action spotting
Others
NLP
Text classfication
Question answering
Large languag model
Sentiment analysis
Others
Training
Reinforcement learning
Gumbel-softmax
Improved SemHash
Knowledge distillation
Challenges
Huge storage space
Lighter-weight
Architecture Design
co-design with hardware
Stability
Combining with popular models
How to be dynamic
Dynamic Architecture
Cascading
Early exiting
Layer skipping
Dynamic prunning
Dynamic parameter
Convolution kernel
Linear transformation
Activation function
Dynamic input processing
Image input
Sequence input
0 Comments
Next Page
Recommended for you
See more
