Adapting Machine Learning Parameters via Entropy Loss for Domain Shift
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Solution Overview
Problem
Machine learning systems, especially neural networks, face significant performance deterioration after a domain shift, where the data encountered during deployment differs from the training data, leading to challenges in adapting to new domains without reliable annotations.
Innovation Solution
A computer-implemented method for determining an output signal by adapting the parameters of a machine learning system based on a loss function characterizing entropy or cross-entropy, allowing the system to adapt to new domains using unlabeled data from the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning system is deployed in a new domain without retraining, then deployment speed is improved, but prediction performance deteriorates due to domain shift
Solution Approach 1:
The system performs preliminary adaptation actions during the deployment phase by computing adaptation parameters using a loss function that measures domain shift. This preliminary adaptation allows the system to adjust to the new domain before final inference, resolving the contradiction between fast deployment and maintained performance.
Solution Approach 2:
The invention changes the parameters of the machine learning system dynamically during deployment by computing adaptation parameters based on the loss function. This parameter adjustment allows the system to adapt to domain shift without retraining, maintaining prediction performance while enabling rapid deployment.
2Reliability
If machine learning system is retrained on new domain data, then prediction performance is improved, but time consumption increases
Solution Approach 1:
The invention extracts only the necessary adaptation information from the new domain data by using unlabeled target domain data to compute adaptation parameters. This extraction approach avoids the time-consuming process of full retraining while still achieving performance improvement through targeted parameter adjustment.
Solution Approach 2:
Instead of performing complete retraining (excessive action), the system applies partial adaptation by computing adaptation parameters based on a loss function that measures domain shift. This partial action is sufficient to improve prediction performance without the time cost of full retraining.
3Measurement precision
If adaptation is performed using labeled data from source domain, then adaptation accuracy is improved, but data requirement complexity increases
Solution Approach 1:
The invention inverts the traditional adaptation approach by using unlabeled target domain data instead of labeled source domain data. The loss function is designed to measure domain shift and compute adaptation parameters based on this inverted approach, achieving adaptation accuracy without requiring complex labeled data from the source domain.
Solution Approach 2:
The system uses disposable unlabeled data from the target domain for adaptation, replacing the need for valuable and complex labeled source domain data. This approach simplifies data requirements while maintaining adaptation accuracy through the loss function-based parameter computation.
Data Source
AI summary
A computer-implemented method for determining an output signal characterizing a semantic segmentation and/or an instance segmentation of an image. The method includes: determining a first intermediate output signal from a machine learning system, wherein the first intermediate output signal characterizes a semantic segmentation and/or an instance segmentation of the image; adapting parameters of the machine learning system based on a loss function, wherein the loss function characterizes an entropy or a cross-entropy of the first intermediate output signal; determining the output signal from the machine learning system based on the image and the adapted parameters.