摘要
本文重點研究圖像分類問題，提出了一種半監督學習方法，用於處理只有少量標記數據但有大量未標記數據的情況。半監督學習是一個重要的機器學習研究問題，因為它結合了標記數據和未標記數據來學習預測模型。此外，深度學習在許多應用領域已經顯示出有希望的結果，但深度學習的一個重要要求是在模型訓練期間使用大量的訓練數據。深度學習的成功激勵我們使用深度學習技術來開發所提出的半監督學習算法。這項工作建議學習深度學習的嵌入空間，以便投射到新空間的數據可以更容易分開。為了充分利用未標記的數據，我們使用自我訓練技術和少量學習架構來開發模型。在實現中，幾射學習架構是三重網絡，它通過給出不同和相似對的三元組來學習距離函數。所提出的方法包括以下步驟。首先，我們在三聯網絡上進行監督學習並獲得良好的嵌入。我們將獲得的嵌入轉移到分類器。其次，我們使用自我訓練將未標記的數據迭代地包含到模型中並增加列車集的數據大小。由於該方法可以有效地使用未標記的數據，因此可以減少準備標記數據的工作量並減少深度學習技術的使用障礙。
並列摘要
This thesis focuses on image classification problem, and propose a semi-supervised learning method to deal with the situations where only a few labeled data but enormous unlabeled data are available. Semi-supervised learning is an important machine learning research problem as it combines labeled data as well as unlabeled data to learn a predictive model. Moreover, deep learning has shown promising results in many application domains, but one important requirement for deep learning is to use enormous training data during model training. The success of deep learning inspires us to use deep learning technique to develop the proposed semi-supervised learning algorithm. This work proposes to learn an embedding space with deep learning, so that the data projected onto the new space could be easier separated. To fully utilize unlabeled data, we use self-training technique and few-shot learning architecture to develop the model. In the implementation, the few-shot learning architecture is triplet network, which learns distance functions by giving a triplet of dissimilar and similar pairs. The proposed method comprises the following steps. First, we perform supervised learning on triplet network and obtain good embeddings. We transfer the obtained embedding to the classifier. Second, we use self-training to include unlabeled data into the model iteratively and increase data size of train set. Since this method can use unlabeled data effectively, it is possible to reduce the effort for preparation of labeled data and to reduce the barriers to the usage of deep learning technology.