Unverified50% confidenceSolutionExact time
领域自适应作为迁移学习子领域，常用方法包括对抗训练（DANN）、最大均值差异最小化（MMD）以及基于元学习框架（如MAML）的少样本适应
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50%
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Long-term
Relevance
7/19/2026
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Unverified小样本学习（Few-Shot Learning）的主流方法包括基于度量学习的Siamese Network、基于元学习的MAML，以及结合预训练大模型的提示微调75% similarUnverifiedIn transfer learning, there are typically two strategies: freezing most layers of the pretrained model and only training the classification head (Feature Extraction), or initializing with pretrained weights and fine-tuning all layers with a smaller learning rate (Fine-Tuning).73% similarUnverified领域适应的标准路径是微调，使用 LoRA 或全量微调对预训练权重进行领域适应，以 WER（词错误率）指标评估效果72% similarUnverified领域自适应预训练（DAPT）指在通用基础模型上用领域特定语料继续训练，能以较低成本显著提升特定任务性能71% similarUnverifiedDomain shift refers to the systematic difference between the distribution of training data and the distribution of data in actual deployment scenarios, and is one of the core challenges in transfer learning and AI engineering deployment71% similar
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