[ICML19] Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
谷歌等一篇名为《挑战无监督分离式表征的常见假设》的论文，表明 (没有归纳偏置的) 无监督方法学不到可靠的分离式表征 (Disentangled Representations) 。本篇是ICML2019的两篇best paper之一。 Abstract 分离式表征的无监督学习背后的关键思想是，真实世界的数据是由几个解释变量生成的，这些变量可以用无监督学习算法恢复。本文对这一领域的最新进展进行了冷静
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相关文章
- 1. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations学习与理解
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