中文标题#
以中心為導向的原型對比聚類
英文标题#
Center-Oriented Prototype Contrastive Clustering
中文摘要#
對比學習由於其區分性表示而在聚類任務中被廣泛使用。 然而，類間的衝突問題難以有效解決。 現有方法嘗試通過原型對比來解決這個問題，但硬原型的計算與真實聚類中心之間存在偏差。 為了解決這個問題，我們提出了一種以中心為導向的原型對比聚類框架，該框架包括一個軟原型對比模塊和一個雙一致性學習模塊。 簡而言之，軟原型對比模塊使用樣本屬於聚類中心的概率作為權重來計算每個類別的原型，同時避免類間衝突並減少原型漂移。 雙一致性學習模塊分別對同一樣本的不同變換和不同樣本的鄰域進行對齊，確保特徵具有變換不變的語義信息和緊密的類內分布，同時為原型的計算提供可靠的保證。 在五個數據集上的大量實驗表明，所提出的方法相比 SOTA 是有效的。 我們的代碼已發布在 https://github.com/LouisDong95/CPCC。
英文摘要#
Contrastive learning is widely used in clustering tasks due to its discriminative representation. However, the conflict problem between classes is difficult to solve effectively. Existing methods try to solve this problem through prototype contrast, but there is a deviation between the calculation of hard prototypes and the true cluster center. To address this problem, we propose a center-oriented prototype contrastive clustering framework, which consists of a soft prototype contrastive module and a dual consistency learning module. In short, the soft prototype contrastive module uses the probability that the sample belongs to the cluster center as a weight to calculate the prototype of each category, while avoiding inter-class conflicts and reducing prototype drift. The dual consistency learning module aligns different transformations of the same sample and the neighborhoods of different samples respectively, ensuring that the features have transformation-invariant semantic information and compact intra-cluster distribution, while providing reliable guarantees for the calculation of prototypes. Extensive experiments on five datasets show that the proposed method is effective compared to the SOTA. Our code is published on https://github.com/LouisDong95/CPCC.
文章页面#
PDF 獲取#
抖音掃碼查看更多精彩內容