[ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
谷歌等一篇名爲《挑戰無監督分離式表徵的常見假設》的論文，表明 (沒有歸納偏置的) 無監督方法學不到可靠的分離式表徵 (Disentangled Representations) 。本篇是ICML2019的兩篇best paper之一。 Abstract 分離式表徵的無監督學習背後的關鍵思想是，真實世界的數據是由幾個解釋變量生成的，這些變量可以用無監督學習算法恢復。本文對這一領域的最新進展進行了冷靜
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相關文章
- 1. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations學習與理解
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