AI Image Classification Model for Test-Time Domain Adaptation
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Solution Overview
Problem
Existing AI image classification models face challenges in adapting to domain shifts, where the characteristics of images in the target domain differ from those in the source domain, leading to suboptimal classification performance.
Innovation Solution
A method involving a weight-space ensemble operation using exponential moving average and large dropout to update parameters of a first AI model, combined with a knowledge transfer network that calculates an objective function based on cross-entropy and Kullback-Leibler divergence, enabling efficient test-time adaptation for domain-shifted images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test-time adaptation is performed to adapt the AI classifier model to target domain images, then classification performance in the target domain is improved, but computational complexity and processing time increase
Solution Approach 1:
The adaptation process is segmented into two distinct phases: a training phase where the AI model learns from source domain images, and a test-time adaptation phase where the model adapts to target domain images using a simplified procedure. This segmentation allows the complex training process to be separated from the lighter test-time adaptation, improving reliability while controlling computational complexity during inference.
Solution Approach 2:
The patent applies parameter changes by updating the AI model's parameters during test-time adaptation using a simplified adaptation procedure that leverages the pre-trained model's parameters. This allows the model to adapt to target domain characteristics without retraining from scratch, thereby improving classification performance while reducing the computational burden compared to full retraining.
2Reliability
If test-time adaptation is performed to adapt the AI classifier model to target domain images, then classification performance in the target domain is improved, but processing time increases
Solution Approach 1:
The AI model is pre-trained on source domain images before being deployed for target domain classification. This preliminary training action establishes a solid foundation that reduces the amount of adaptation needed during test time. The model only needs to perform lighter parameter updates rather than complete retraining, thereby improving performance while minimizing processing time delays.
3Reliability
If the AI classifier model is trained on source domain images, then the model achieves good performance on source domain tasks, but performance degrades when applied to target domain images with different characteristics
Solution Approach 1:
The patent creates a universal AI model that can function effectively in both source and target domains. The model is pre-trained on source domain images to achieve strong source domain performance, then equipped with a test-time adaptation mechanism that enables it to adapt to target domain images with different characteristics. This multi-functionality allows the same model to serve both domains effectively.
Solution Approach 2:
The model transitions from a static pre-trained state to a dynamic adaptable state during test time. The test-time adaptation procedure allows the model's parameters to be updated based on target domain characteristics, making the model dynamically adjustable rather than fixed. This dynamic capability enables the model to maintain source domain performance while adapting to target domain requirements.
Data Source
AI summary
A method for determining a class of an image includes: receiving a first prediction result for the class from a first classifier and a second prediction result for the class from a second classifier, updating an artificial intelligence (AI) model of the first classifier based on the first prediction result and the second prediction result, and inferring the class of the image using the updated AI model are provided.