COSMO ARTIFICIAL INTELLIGENCE – AI LIMITED (20240303299). SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS simplified abstract
SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS
Organization Name
COSMO ARTIFICIAL INTELLIGENCE – AI LIMITED
Inventor(s)
GIULIO Evangelisti of ROMA (IT)
SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303299 titled 'SYSTEMS AND METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS AND USE OF TRAINED GENERATIVE ADVERSARIAL NETWORKS
The present disclosure pertains to computer-implemented systems and methods for training and utilizing generative adversarial networks. In one implementation, a system for training a generative adversarial network may involve at least one processor providing a first set of images containing representations of a feature-of-interest and indicators of the feature's locations, using this set to train an object detection network. The processor(s) may then provide a second set of images with representations of the feature-of-interest, apply the trained object detection network to produce detections of the feature, and obtain manual verifications of true and false positives for these detections. These verifications are used to train a generative adversarial network, which is then retrained using additional images, detections, and manual verifications.
- System for training generative adversarial networks
- Utilizes object detection networks to identify features-of-interest
- Incorporates manual verifications of detections to improve training
- Iterative process of training and retraining the generative adversarial network
- Enhances the accuracy and efficiency of feature detection
Potential Applications: - Image recognition and analysis - Surveillance systems - Medical imaging for identifying specific features - Autonomous vehicles for object detection - Quality control in manufacturing processes
Problems Solved: - Improving the accuracy of feature detection in images - Streamlining the training process for generative adversarial networks - Enhancing the performance of object detection systems
Benefits: - Increased precision in identifying features-of-interest - Reduction in false positives and negatives - Automation of manual verification processes - Enhanced overall performance of computer vision systems
Commercial Applications: Title: Advanced Image Recognition System for Enhanced Object Detection This technology can be utilized in various industries such as security, healthcare, automotive, and manufacturing for improving image analysis and object detection processes. The market implications include increased efficiency, accuracy, and reliability in feature identification tasks.
Prior Art: Readers interested in exploring prior art related to this technology may refer to research papers, patents, and publications in the fields of computer vision, machine learning, and artificial intelligence.
Frequently Updated Research: Researchers in the fields of computer vision and machine learning are continuously exploring new techniques and algorithms to enhance the performance of generative adversarial networks for improved object detection and image analysis. Stay updated on the latest advancements in this area for cutting-edge applications.
Questions about Generative Adversarial Networks: 1. How do generative adversarial networks differ from traditional neural networks in image processing tasks? Generative adversarial networks consist of two neural networks, the generator, and the discriminator, which work in tandem to generate realistic images. In contrast, traditional neural networks are typically used for classification or regression tasks.
2. What are some common challenges faced when training generative adversarial networks for object detection applications? Training generative adversarial networks for object detection can be challenging due to issues such as mode collapse, training instability, and balancing the generator and discriminator networks for optimal performance.
Original Abstract Submitted
the present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks. in one implementation, a system for training a generative adversarial network may include at least one processor that may provide a first plurality of images including representations of a feature-of-interest and indicators of locations of the feature-of-interest and use the first plurality and indicators to train an object detection network. further, the processor(s) may provide a second plurality of images including representation of the feature-of-interest, and apply the trained object detection network to the second plurality to produce a plurality of detections of the feature-of-interest. additionally, the processor(s) may provide manually set verifications of true positives and false positives with respect to the plurality of detections, use the verifications tr train a generative adversarial network, and retrain the generative adversarial network using at least one further set of images, further detections, and further manually set verifications.
