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Efficient TestTime Adaptation for SuperResolution with SecondOrder Degradation and ReconstructionZeshuai Deng1 Zhuokun Chen1 2 Shuaicheng Niu1 Thomas H. Li5 Bohan Zhuang3 Mingkui Tan1 2 4 1South China
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Efficient test-time adaptation is used to enhance the performance of machine learning models during inference by allowing them to adapt to specific characteristics of the input data without requiring extensive retraining.
Researchers, developers, and practitioners who implement machine learning models that require dynamic adaptation to varying input data conditions are required to consider efficient test-time adaptation.
To fill out efficient test-time adaptation, one must identify the parameters of the model that need adjustment, collect relevant test data, implement the adaptation methods, and validate the results.
The purpose of efficient test-time adaptation is to improve model accuracy and robustness in real-world applications by allowing the model to refine its predictions based on the latest data inputs.
Information that must be reported includes the model architecture, the adaptation techniques used, performance metrics before and after adaptation, and the characteristics of the input data that influenced the adaptation process.
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