林軒田Hung-Chieh Fang方泓傑2024-09-262024-09-262024https://ntu.primo.exlibrisgroup.com/permalink/886NTU_INST/14poklj/alma991039261578504786https://scholars.lib.ntu.edu.tw/handle/123456789/721570指導教授：林軒田Universal Domain Adaptation (UniDA) seeks to address the challenges posed by distribution shifts between source and target domains without assuming any relationships between their label sets. In this paper, we demonstrate that misalignment in Domain Adaptation (DA) can occur in two scenarios. The first scenario, which we term as implicit misalignment, arises from a source-biased feature space. The second scenario, termed explicit misalignment, occurs when feature alignment methods mistakenly align incorrect features, further exacerbating the misalignment issue. We further demonstrate that UniDA can intensify these two types of misalignment. Previous works have primarily addressed the explicit misalignment issue by designing improved methods for uncertainty measurement to down-weight the influence of private samples in feature alignment. However, these methods often fall short when implicit misalignment is substantial. To address this, we propose a source-label-free target incorporation module that leverages self-supervised learning to mitigate implicit misalignment. Unlike traditional methods, self-supervised learning promotes a broader diversity of guidance, reducing the likelihood of incorrect feature alignments. Our experimental results on the Office-Home and Office31 dataset with different settings indicate that our approach is more effective at handling problems with large implicit misalignment.通用域適應旨在解決源域和目標域之間的分佈偏移，且不假設它們的標籤集之間有任何關係所帶來的挑戰。在本文中，我們展示了域適應中的偏差可能出現在兩種情境中。我們將第一種情境稱為隱性偏差，這種偏差源於源偏置的特徵空間。第二種情境，我們稱之為顯性偏差，發生在特徵對齊方法錯誤地對齊了不正確的特徵，進一步加劇了偏差問題。我們進一步展示了通用域適應可能加劇這兩種類型的偏差。以往的研究主要通過設計改進的不確定性測量方法來解決顯性偏差問題，以降低特徵對齊中私有樣本的影響。然而，當隱性偏差較大時，這些方法往往表現不佳。為此，我們提出了一種無源目標融入模塊，該模塊利用自監督學習來緩解隱性偏差。與傳統方法不同，自監督學習促進了更廣泛的指導多樣性，降低了不正確特徵對齊的可能性。我們在Office-Home 和Office31 數據集上的不同設置下進行的實驗結果表明，我們的方法在處理大規模隱性偏差問題上更為有效。Domain Adaptation域適應Universal Domain Adaptation通用域適應Machine Learning機器學習Self-supervised Learning自監督學習thesis