New theory explains train-validation separation in pretrained models

Researchers have proposed a new dynamic structural account for how train-validation separation emerges in pretrained models. This phenomenon, where performance on training data diverges from validation data, is explained by the model's adaptation shifting its focus from broadly reusable features to more specific ones that transfer poorly to unseen data. The study uses controlled simulations with ResMLP, natural language processing models like RoBERTa, DeBERTa, and Qwen, and vision models like ResNet-18 to demonstrate that observable structural evolution can predict this separation without needing direct access to validation examples. AI

IMPACT Provides a theoretical framework to understand and potentially mitigate overfitting in pretrained models across various domains.

RANK_REASON The cluster contains a research paper detailing a new theoretical account for a phenomenon in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• DeBERTa
• Gotit.pub
• Hugging Face
• Qwen
• ResMLP
• ResNet-18
• Roberta
• ScienceCast

• paper
• model release

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