研究生: |
劉又寧 Liu, You-Ning |
---|---|
論文名稱: |
Unsupervised Clustering Based on Alpha-Divergence Unsupervised Clustering Based on Alpha-Divergence |
指導教授: |
黃聰明
Huang, Tsung-Ming |
口試委員: |
黃聰明
Huang, Tsung-Ming 陳建隆 林敏雄 |
口試日期: | 2022/01/25 |
學位類別: |
碩士 Master |
系所名稱: |
數學系 Department of Mathematics |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 英文 |
論文頁數: | 22 |
英文關鍵詞: | Alpha-Divergence, Deep Learning, Deep Clustering, Contrastive Learning, ResNet, Tsallis Entropy, KL Divergence, Shannon Entropy |
DOI URL: | http://doi.org/10.6345/NTNU202200195 |
論文種類: | 學術論文 |
相關次數: | 點閱:115 下載:6 |
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Recently, many deep learning methods have been proposed to learning representations or clustering without labelled data. Using the famous ResNet[1] backbone as an effective feature extractor, we present a deep efficient clustering method that optimizes the data representation and learn the clustering map jointly. Despite the many successful applications of Kullback–Leibler divergence and Shannon entropy, we use alpha-divergence and Tsallis entropy to be an extension of the common loss functions. For detailed interpretation , we further analyze the relation between the clustering accuracy and the distinct alpha values. Also, we achieve 53.96% test accuracy on CIFAR-10[2] dataset, 27.24% accuracy on CIFAR-100-20[2] dataset in unsupervised tasks
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