領域泛化的目標是藉由在多個源域中學習出能夠泛化於看不見的目標域模型。
在假設目標域的分布和源域不相同的前提下，先前的方法大多解決了領域外泛化
的問題，卻很少關注在源域的領域內泛化表現。而我們認為由於目標域是無法預
見地，且其分布可能會與源域的分布相當接近，因此領域內及領域外的泛化能力
是同樣重要的。另外，當源域出現不一致或是嘈雜的真實標籤時，模型的穩健性
也是重要的議題。因此，在本文中，我們提出了利用原型對齊與協作注意力模組
的對比式學習架構來解決圖片分類在穩健領域內與領域外之領域泛化的問題。首
先，我們設計一個基於距離的對比式學習來分開模糊的類別到一定距離以提升領
域外的表現。再者，我們提出利用原型對齊來將每個類別的特徵表徵對齊到對應
的原型上以提升領域內的表現。最後，我們提出一個全新的協作注意力模組來運
用正向與反向學習的好處以提升模型的穩健程度。實驗結果在兩個基準下顯示我
們的方法不僅在領域內的表現相當有競爭力，在領域外的表現以及具嘈雜標籤的
情況下更是優於之前的方法。

Domain generalization focuses on generalizing a model learned from multiple source domains to unseen target domains. Assuming the target domains distribute differently from the source domains, most previous methods address the out-of-domain generalization issue but slightly concern the in-domain performance on the source domains. Because the target domains are unseen and may distribute similarly with the source domains, we believe both the in-domain and out-of-domain performances are equally important. The model robustness also raises concerns when there exist inconsistent or noisy ground truth labels in the source domains. Therefore, in this thesis, we propose a contrastive learning framework with prototype alignment and collaborative attention to address the robust in-domain and out-of-domain generalization issue for image classification. We first design a margin-based contrastive learning to boost the out-of-domain performance by pushing the ambiguous classes apart by at least a margin. Next, we propose using prototype alignment to support the in-domain performance by aligning the latent feature representation of each class to the corresponding class prototype. Finally, we propose a novel collaborative attention method by leveraging the strength from both positive and negative learnings to enhance the model robustness. Experimental results on two benchmarks show that our method achieves competitive in-domain performance and outperforms previous methods in the out-of-domain and noisy label scenarios.

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