Abstract
We propose and justify the development of neural network architectures for training image analysis systems with a dynamically configurable calculation structure. The proposed systems, trained in accordance with the principle of least action, are useful for increasing the processing speed of large amounts of data and can help overcome other shortcomings of deep architectures. The problem of image classification is considered in detail, the solution of which reduces to training a network agent that operates in an environment of classifiers and indirectly perceives images through them. The proposed approach can be used to create algorithms in systems for automatic image analysis, where the critical characteristic is the average processing time of one frame, for example, in systems for image indexing based on their content.
© 2020 Optical Society of America
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription