18479723. TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION simplified abstract (QUALCOMM Incorporated)
Contents
- 1 TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION
Organization Name
Inventor(s)
TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18479723 titled 'TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION
Simplified Explanation
The abstract describes a computer-implemented method that involves adding auxiliary networks to partitions associated with a main network, training these auxiliary networks with training data, adapting them with test data, and classifying inputs based on the adapted networks.
- The method involves adding auxiliary networks to partitions of a main network.
- The auxiliary networks are trained with training data to adapt to a test distribution.
- The auxiliary networks are further adapted with test data to better fit the test distribution.
- Inputs received at a model are classified based on the adapted auxiliary networks.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and financial forecasting.
Problems Solved
This technology helps improve the adaptability and accuracy of neural networks to different distributions of data, enhancing their performance in various tasks.
Benefits
The method allows for better adaptation of neural networks to new data distributions, leading to improved classification accuracy and performance.
Potential Commercial Applications
This technology could be valuable in industries such as healthcare, finance, and e-commerce for tasks like medical image analysis, fraud detection, and personalized recommendations.
Possible Prior Art
One possible prior art could be techniques for domain adaptation in machine learning, where models are adapted to new domains with limited labeled data.
Unanswered Questions
How does this method compare to existing techniques for domain adaptation in neural networks?
This article does not provide a direct comparison to existing techniques for domain adaptation in neural networks.
What are the computational requirements of implementing this method on large-scale datasets?
This article does not address the computational requirements of implementing this method on large-scale datasets.
Original Abstract Submitted
A computer-implemented method includes adding an auxiliary network of a group of auxiliary networks to a respective partition of a group of partitions associated with a main network. The method also includes training each of the group of auxiliary networks with training data to adapt to a test distribution. The method further includes adapting each of the group of auxiliary networks with test data to adapt to the test distribution. The method still further includes classifying an input received at a model based on adapting each of the group of auxiliary networks. The model may include the group of partitions and the group of auxiliary networks.
