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研究生： 龎渝庭
Pang, Yu-Ting
論文名稱： 領域自適應語義分割的自引導對抗學習
Self-­guided Adversarial Learning for Domain Adaptive Semantic Segmentation
指導教授： 許秋婷
Hsu, Chiou-Ting
口試委員: 林嘉文
Lin, Chia-Wen
林彥宇
Lin, Yen-Yu
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 28
中文關鍵詞： 非監督式之領域自適應 、語意分割 、自引導對抗式學習
外文關鍵詞： Unsupervised domain adaptation, Semantic segmentation, Self-guided adversarial learning
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    • 非監督域適應被引入來將語意分割模型從具有標籤的合成資料泛化到不具有標籤的現實場景資料。儘管過去有許多方法被提出來減少合成資料與現實場景的域差異，然而，現實場景資料的分割結果品質仍然高度地不一致。在本文中，我們討論了兩個阻礙過去方法獲得令人滿意結果的主要問題，並提出一種新穎的自我引導對抗學習架構以提升現有分割模型的領域適應能力。首先，為了處理現實場景中不可預測的資料變異，我們通過選擇可靠的目標像素作為指導來引導其他像素的適應過程，開發出一種自我引導的對抗學習方法。其次，為了解決類別不平衡的問題，我們設計了一個獨立處理各類別的選擇策略，並將此想法與類別對抗學習相結合成一個框架。此外，我們也進一步將自我引導的對抗式學習導入至現有的自我蒸餾方法中，並進一步提升分割模型的結果品質。實驗結果顯示我們所提出的方法在幾個標準的數據集上，顯著改進了過去在非監督域適應的方法。

    Unsupervised domain adaptation has been introduced to generalize semantic segmentation models from labeled synthetic images to unlabeled real-world images. Although much effort was devoted to minimize the cross-domain gap, the segmentation results on real-world data remain highly unstable. In this thesis, we discuss two main issues which hinder previous methods from achieving satisfactory results and propose a novel self-guided adversarial learning to leverage the capability of domain adaptation. Firstly, to deal with the unpredictable data variation in the real-world domain, we develop a self-guided adversarial learning method by selecting reliable target pixels as guidance to lead the adaptation of the other pixels. Secondly, to address the class-imbalanced issue, we devise the selection strategy in each class independently and incorporate this idea with a class-level adversarial learning in a unified framework. Moreover, we demonstrate that incorporating the self-guided adversarial learning into self-distillation further boosts the performance. Experimental results show that the proposed method significantly improves the previous methods on several benchmark datasets.

    摘要---ii Abstract---iii Acknowledgements---iv 1 Introduction---1 2 Related work---5 2.1 Adversarial Training---5 2.2 Self-training---7 2.3 Self-distillation---7 3 Proposed method---8 3.1 Cross-domain Adaptation---9 3.2 Class-balanced Guide-pixel Selection---10 3.3 Self-guided Adaptation---11 3.4 Training Objectives---12 3.5 Self-distillation---13 4 Experiment---14 4.1 Datasets and Evaluation Metrics---14 4.2 Implementation Details---15 4.2.1 Data-preprocessing---15 4.2.2 Network architecture and Hyper-parameter settings---16 4.3 Quantitative results---17 4.3.1 Comparison with other methods---17 4.3.2 Ablation Study---18 4.3.3 Class-wise feature discrimination---19 4.4 Qualitative results---20 4.5 Discussion---22 5 Conclusion---24 References---25

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