近日，清华大学大数据研究中心机器学习研究部开源了一个高效、简洁的迁移学习算法库 Transfer-Learn，并发布了第一个子库——深度领域自适应算法库（DALIB）。
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Domain Adversarial Neural Network (DANN)
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Deep Adaptation Network (DAN)
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Joint Adaptation Network (JAN)
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Conditional Domain Adversarial Network (CDAN)
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Maximum Classifier Discrepancy (MCD)
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Margin Disparity Discrepancy (MDD)
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统计距离。通过最小化源领域和目标领域分布的统计距离，实现不同领域特征分布对齐。例如深度适配网络 DAN、联合适配网络 JAN。
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对抗训练。领域对抗网络 DANN 是最早的工作，它引入领域判别器，鼓励特征提取器学习领域无关的特征。在 DANN 的基础上衍生出了一系列方法，例如条件领域对抗网络 CDAN、最大分类器差异 MCD。
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理论启发。通过严格的理论推导，得到可以显式控制迁移学习泛化误差的算法，如间隔分歧散度 MDD 等。
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复用性差。领域自适应方法和模型架构、数据集耦合在一起，不利于领域自适应方法在新的模型、数据集上复用。
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稳定性差。部分对抗训练方法随着训练进行，准确率会大幅度下降。
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