论文笔记：Representation Learning with Contrastive Predictive Coding
对于无监督学习来说，互信息是一个非常重要的指标，它衡量了两个随机变量之间的相关性。在无监督学习中，利用对互信息的优化，通常我们能够得到更加好的特征表示。 要做什么 特征抽取是无监督学习的重要部分，旨在对大量的无标注数据样本进行训练，最后能够得到一个编码器E，将每一个样本 x x x编码为一个好的向量表示 z z z，那么如何衡量这个表示是否好呢？自编码器(AutoEncoder)告诉我们, 希望编
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相关文章
- 1. 论文阅读：Representation Learning with Contrastive Predictive Coding
- 2. Representation Learning with Contrastive Predictive Coding
- 3. Predictive learning vs. representation learning
- 4. PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning --- 论文笔记
- 5. 论文笔记：Contrastive Learning for Image Captioning
- 6. 【论文笔记】Representation Learning by Learning to Count
- 7. 论文阅读学习 - Deep Representation Learning with Target Coding
- 8. 论文笔记: Inductive Representation Learning on Large Graphs
- 9. Inductive Representation Learning on Large Graphs论文笔记
- 10. 论文笔记：Inductive Representation Learning on Large Graphs