View source for OPTIMIZATION-BASED PARAMETRIC MODEL FITTING VIA DEEP LEARNING: abstract simplified (17719335)

*This abstract for appeared for patent application  number 17719335 Titled 'OPTIMIZATION-BASED PARAMETRIC MODEL FITTING VIA DEEP LEARNING'

==Simplified Explanation==

The abstract describes a neural optimizer that can be used for various fitting problems. It can run quickly and efficiently without the need for manual input, and it incorporates information from previous iterations to improve accuracy. The optimizer controls the learning rate of each parameter individually, ensuring robustness and fast convergence. It combines gradient descent with a method that rapidly reduces the fitting energy. Overall, the abstract highlights the capabilities and advantages of this neural optimizer.


==Original Abstract Submitted==

A neural optimizer is disclosed that is easily applicable to different fitting problems, can run at interactive rates without requiring significant efforts, does not require hand crafted priors, carries over information about previous iterations of the solve, controls the learning rate of each parameter independently for robustness and convergence speed, and combines updates from gradient descent and from a method capable of very quickly reducing the fitting energy. A neural fitter estimates the values of the parameters Θ by iteratively updating an initial estimate Θ.