Unsupervised Feature Learning via Non-parametric Instance Discrimination
这篇文章是cvpr2018的spotlight。是伯克利、香港中文大学、亚马逊联合发表的论文。 论文：Unsupervised Feature Learning via Non-parametric Instance Discrimination. 摘要和介绍中指出，通过对有监督的图像分类结果的观察，发现图像分类的依据不是图像的语义注释，而是数据本身的视觉相似性，换句话说，视觉相似性不是从语义注释
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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