Qualcomm incorporated (20240160926). TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION simplified abstract
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 20240160926 titled 'TEST-TIME ADAPTATION VIA SELF-DISTILLED REGULARIZATION
Simplified Explanation
The abstract describes a computer-implemented method that involves adding an auxiliary network to a partition of a main network, training the auxiliary networks with data, adapting them to a test distribution, and using them to classify input data.
- The method adds auxiliary networks to partitions of a main network.
- The auxiliary networks are trained with data to adapt to a test distribution.
- The trained auxiliary networks are adapted with test data to further adapt to the test distribution.
- The input data is 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 performance of machine learning models by adapting them to specific test distributions, leading to more accurate predictions.
Benefits
The benefits of this technology include enhanced model accuracy, improved generalization to new data, and increased adaptability to changing environments.
Potential Commercial Applications
Potential commercial applications of this technology include personalized recommendation systems, fraud detection algorithms, and predictive maintenance solutions.
Possible Prior Art
One possible prior art could be the use of ensemble learning techniques to improve model performance by combining multiple models.
What are the specific training data used to adapt the auxiliary networks?
The specific training data used to adapt the auxiliary networks are not mentioned in the abstract. It would be helpful to know the sources and types of data used for training to understand the effectiveness of the adaptation process.
How does the method handle potential biases in the test distribution during adaptation?
The abstract does not provide information on how potential biases in the test distribution are addressed during the adaptation process. Understanding how biases are mitigated could shed light on the robustness of the method in real-world applications.
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.
