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Research Question: Can continuous learning methods help with LAD shift adaptation?
We investigate whether continuous learning-based shift adaption can help mitigate such negative impacts. Here, we only consider AE and VAE models as they are relatively better methods for handling distribution shifts.
Results of continuous learning for LAD shift adaptation. The best and worst results are highlighted by green and red background
Answer to Research Question:
Continuous learning can enhance the performance of LAD models in shift environments but not always. The selection of continuous learning methods and the labeling budget highlight the effects on the performance of the produced LAD models.
Cross Validation:
We run all experiments using the 10-fold cross-validation and conduct statistical significance analysis. The refined result figure is shown below. The figure displays an error bar chart from the shift evaluation in our paper. From the results, we observe that the error bars do not exhibit any distinct unusual statistics, with all deviations being less than 5%, demonstrating the good reliability of our evaluation results.
We further conducted an independent samples t-test to evaluate the statistical significance of differences between the in-distribution and six-shift log data. The p-value for the F1-score of LAD models in each shift scenario is 1E-03, 1.0E-03, 1.3E-03, 3.9E-03, 4.7E-05, and 2.1E-05, respectively. The result illustrates that all the shift logs have statistically significant differences from the baseline. The analysis provides a robust statistical foundation for asserting that the shift logs and their characteristics can significantly impact the LAD models.Â
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