迁移学习中，过拟合、欠拟合、欠适配、负迁移等关键问题与挑战交错叠加。
首先，在拟合观测数据所服从的未知概率分布时存在模型的过拟合或欠拟合问题；
其次，在领域间适配不同的未知概率分布时存在模型的欠适配或负迁移问题：欠
适配是指跨领域的概率分布失配问题未能充分修正，负迁移是指辅助领域任务对
目标领域任务有负面效果。本文重点面向欠拟合、欠适配、负迁移等问题挑战，
分析原因并设计针对性的学习方法(Transfer learning involves several critical issues and challenges: overfitting, under-
fitting, under-adaptation, and negative-transfer. Overfitting and underfitting may hap-
pen when modeling the unknown probability distribution based on observed data; Under-
adaptation and negative-transfer may happen when adapting the unknown probability dis-
tributions across domains: under-adaptation refers to the condition that the distribution
mismatch cannot be corrected sufficiently; negative-transfer refers to the condition that
the auxiliary task deteriorates the target task unintentionally. This thesis addresses the
underfitting, under-adaptation, and negative-transfer issues, analyzes the intrinsic causes,
and designs specific learning models)