Unsupervised Feature Learning via Non-Parametric Instance Discrimination
论文地址：Unsupervised Feature Learning via Non-Parametric Instance Discrimination github代码:NCE代码 摘要: 在有标签数据上训练的神经网络分类器能够很好的捕捉图片间的视觉相似性。文章假设：我们能通过训练基于实例（将每一个样本视为单独的类别）的分类器代替基于类别的分类器，得到可以捕捉视觉相似性的特征表达。我们将其总结
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相关文章
- 1. [解读] Unsupervised Feature Learning via Non-Parametric Instance Discrimination
- 2. Unsupervised Feature Learning via Non-parametric Instance Discrimination
- 3. 论文阅读笔记：Unsupervised Feature Learning via Non-Parametric Instance Discrimination
- 4. Effective Feature Learning with Unsupervised Learning in MOOCs
- 5. Query-Oriented Multi-Document Summarization via Unsupervised Deep Learning
- 6. 论文阅读:Unsupervised Deep Learning by Neighbourhood Discovery
- 7. 《论文阅读》Unsupervised Person Re-identification via Softened Similarity Learning
- 8. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
- 9. 【小白笔记】Correlation Tracking via Joint Discrimination and Reliability Learning
- 10. Supervised learning and Unsupervised learning