18340996. METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING simplified abstract (Samsung Electronics Co., Ltd.)
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METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING
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METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18340996 titled 'METHOD AND APPARATUS WITH OBJECT ESTIMATION MODEL TRAINING
The abstract describes a method and apparatus for object estimation model training, involving the use of neural networks and cross-correlation loss.
- The method includes generating a cross-correlation loss based on two feature vectors from different neural networks.
- The first feature vector is generated using an interim neural network model based on input data about a target object.
- The second feature vector is generated using a trained neural network based on different input data about the same target object.
- The trained first neural network model is generated by training the interim neural network model based on the cross-correlation loss.
Potential Applications: - Object recognition in computer vision systems - Autonomous driving for identifying objects on the road - Robotics for object manipulation and interaction
Problems Solved: - Improving accuracy and efficiency of object estimation models - Enhancing the performance of neural networks in recognizing objects - Streamlining the training process for neural network models
Benefits: - Increased precision in object recognition tasks - Faster and more reliable object estimation results - Enhanced capabilities for various AI applications
Commercial Applications: Title: Advanced Object Estimation Model for AI Systems This technology can be used in industries such as: - Automotive for autonomous driving systems - Manufacturing for quality control and object detection - Healthcare for medical imaging and diagnostics
Questions about Object Estimation Model Training: 1. How does the cross-correlation loss improve the training of neural network models for object estimation? 2. What are the key differences between the interim neural network model and the trained neural network in this method?
Original Abstract Submitted
A method and apparatus with object estimation model training is provided. The method include generating a cross-correlation loss based on a first feature vector, generated using an interim first neural network (NN) model provided an input based on first input data about a target object, and a second feature vector generated using a trained second neural network provided another input based on second input data about the target object; and generating a trained first NN model, including training the interim first NN model based on the cross-correlation loss.
