An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
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In the realm of out-of-distribution generalization tasks, finetuning has risen as a key strategy. While the most focus has been on optimizing learning algorithms, our research highlights the influence of pre-trained model selection in finetuning on out-of-distribution performance and inference uncertainty. Balancing model size constraints of a single GPU, we examined the impact of varying pre-trained datasets and model parameters on performance metrics like accuracy and expected calibration error. Our findings underscore the significant influence of pre-trained model selection, showing marked performance improvements over algorithm choice. Larger models outperformed others, though the balance between memorization and true generalization merits further investigation. Ultimately, our research emphasizes the importance of pre-trained model selection for enhancing out-of-distribution generalization.
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