Transfer Learning Failure Modes in Domain-Shifted Datasets

Abstract

Transfer learning has become a foundational strategy for accelerating model development across
domains; however, its performance often degrades when applied to datasets that differ significantly
from those used in pre-training. This article examines the failure modes that occur under such domain
shifted conditions and analyzes representational instability, negative transfer, and catastrophic
forgetting during fine-tuning. Through controlled adaptation strategies, the study shows that gradual
unfreezing, curriculum-based training, and projection-based alignment significantly improve
convergence stability and task performance. The findings highlight the importance of designing
adaptive transfer strategies informed by representational divergence patterns rather than applying
uniform fine-tuning approaches.