Dynamic Neural Networks are the upgraded version of Static Neural Networks. They have better decision algorithms and can generate better-quality results. The decision algorithm refers to the improvements to the network. It is responsible for making the right decisions accurately and with the right amount of power. It can learn from its surroundings as well.
They can adapt to various situations and are thus called Dynamic. A Static Neural Network is designed to imitate the human brain. It focuses on problem-solving. But big problems can’t be solved by a Static neural network. For an ever-changing environment, Dynamic Neural Networks are necessary.
Types of Dynamic Neural Networks
1. Sample Wise Dynamic Networks
The Sample Wise Dynamic Networks focus on setting up a network that allocates computation on every kind of sample. It increases power with minimal cost. They adapt to network parameters with fixed computational graphs. This results in a decrease in cost.
2. Spatial Wise Dynamic Networks
The Spatial Wise Dynamic Networks focus on computer vision problems. When image processing in Static Neural Networks, all pixels of the image are not processed. This results in accuracy and computational energy loss. The Spatial Wise Dynamic Networks were built to adapt to the various inferences from different locations of an image.
3. Temporal Wise Dynamic Networks
The Temporal Wise Dynamic Networks focus on adaptive computation. It can be performed with temporal data. They can differentiate between different portions of the sequential data and can also adapt to changes in the data. This results in higher accuracy.
Advantages of Dynamic Neural Networks
The following are the advantages of Dynamic Neural Networks:
- Efficiency: Dynamic Neural Networks are efficient in a way that they have features that can allocate the computation whenever required.
- Improved Representation Power: They have an architecture that is dependent on the input. This results in a big parameter space and improved representation power.
- Compatibility: Dynamic Neural Networks are compatible with various advanced techniques for performance improvement. These include Data Pre-processing, Algorithm optimization, etc.
- Interpretability: There is a large network of neurons in the brain which helps the brain in making decisions. Dynamic Neural Network was inspired by the working of the brain and efforts were made to replicate it.
- Adaptability: Dynamic Neural Networks are adaptable in various conditions. They can change and adapt to their surroundings. This includes deciding on their own.
Applications of Dynamic Neural Networks
- Understanding Social Media Users: This is one of the top applications of Dynamic Neural Networks. It is a great marketing tool for businesses across various sectors. Artificial neurons are used to collect data from different social media accounts of various users. This data is then shared with businesses to evaluate future trends and competition in the market.
- Aerospace Engineering: The key to a successful Aerospace Company is ensuring the passenger’s safety. This can be made possible by using the non-linear and pattern-recognition capabilities of a Dynamic Neural Network. It can be used to check the safety and improve fault diagnostics.
- Healthcare: Dynamic Neural Networks can assist humans in the healthcare industry not only in diagnostics but also in predicting potential health issues and solving them. With the demand for healthcare workers increasing each day, this will be a great technology all over the world.
It can be said, without a doubt, that Dynamic Neural Networks will continue to spread and help humans in various fields. Some of these include Medical, banking, agriculture, etc. They can be used for a wide range of applications including Image Classification, Object Detection, Image Segmentation, etc. They can be used in everyday life as well.