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In the era of transfer learning, training neural networks from scratch isbecoming obsolete. Transfer learning leverages prior knowledge for new tasks,conserving computational resources. While its advantages are well-documented,we uncover a notable drawback: networks tend to prioritize basic data patterns,forsaking valuable pre-learned features. We term this behavior "featureerosion" and analyze its impact on network performance and internalrepresentations.

On consequences of finetuning on data with highly discriminative features