Samsung electronics co., ltd. (20240311634). ELECTRONIC APPARATUS AND METHOD FOR RE-LEARNING TRAINED MODEL simplified abstract
Contents
ELECTRONIC APPARATUS AND METHOD FOR RE-LEARNING TRAINED MODEL
Organization Name
Inventor(s)
ELECTRONIC APPARATUS AND METHOD FOR RE-LEARNING TRAINED MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311634 titled 'ELECTRONIC APPARATUS AND METHOD FOR RE-LEARNING TRAINED MODEL
Simplified Explanation: The patent application describes a method for re-learning a trained model by selectively re-learning parameters associated with a new task.
- Neurons in a trained model are identified for selective re-learning based on a new task.
- Parameters associated with the new task are re-learned for the identified neurons.
- The size of the trained model is dynamically expanded if a preset loss value is reached during re-learning.
Key Features and Innovation:
- Selective re-learning of parameters for specific neurons based on new tasks.
- Dynamic expansion of the trained model to accommodate re-learning.
- Efficient method for updating trained models without starting from scratch.
Potential Applications: The technology can be applied in various fields such as machine learning, artificial intelligence, and data analysis for updating models with new tasks.
Problems Solved: The method addresses the challenge of efficiently updating trained models with new information without the need to retrain the entire model.
Benefits:
- Saves time and computational resources by selectively re-learning parameters.
- Allows for quick adaptation to new tasks without starting from the beginning.
- Improves the accuracy and efficiency of trained models.
Commercial Applications: This technology has potential commercial applications in industries such as healthcare, finance, and e-commerce for improving predictive models and data analysis processes.
Prior Art: Readers can explore prior research on selective re-learning methods in machine learning and neural networks to understand the background of this innovation.
Frequently Updated Research: Stay informed about the latest advancements in selective re-learning techniques in machine learning and artificial intelligence for further insights into this technology.
Questions about Selective Re-Learning of Trained Models: 1. How does selective re-learning of parameters improve the efficiency of updating trained models? 2. What are the potential implications of dynamically expanding the size of the trained model during re-learning?
Original Abstract Submitted
a method for re-learning a trained model is provided. the method for re-learning a trained model includes: receiving a data set including the trained model consisting of a plurality of neurons and a new task; identifying a neuron associated with the new task among the plurality of neurons to selectively re-learn a parameter associated with the new task for the identified neuron; and dynamically expanding a size of the trained model on which the selective re-learning is performed if the trained model on which the selective re-learning has a preset loss value to reconstruct the input trained model.
