Deep Clustering for Unspervised Learning of Visual Features
Abstract 作爲無監督學習方法的一類，聚類已經廣泛地應用在計算機視覺領域。然而只有少數工作將其適用於大範圍的數據集上的視覺特徵端到端訓練。本文我們提出一種方法——DeepCluster，其可以將神經網絡的參數同結果特徵的聚類分配任務一起進行學習。DeepCluster採用k-means標準聚類算法迭代地將特徵分組，並且使用隨後的分配任務作爲監督更新網絡的權重。 我們將DeepCluster應
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相關文章
- 1. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
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