AI RESEARCH
Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications
arXiv CS.LG
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ArXi:2604.27987v1 Announce Type: new Fine-tuning pretrained models has become a standard approach to adapting pretrained knowledge to improve the accuracy on new sparse, imbalance datasets. However, issues arise when optimization falls into a collapsed state, where the model gets stuck, leading to degraded performance and unstable