How to Train Triplet Networks with 100K Identities?論文簡略翻譯
Abstract 摘要 使用大規模數據訓練triplet networks對人臉識別具有挑戰性。由於可能的triplets數量隨着樣本數量的增加而爆炸，以前的研究採用在線硬性負面挖掘（OHNM）來處理它。然而，隨着identities的數量變得非常大，訓練將受到不好的局部最小值（bad local minima）的困擾，因爲難以找到有效的hard triplets 。（triplets 、
相關文章
- 1. 論文水記|How to Train Triplet Networks with 100K Identities?
- 2. (zhuan) How to Train Neural Networks With Backpropagation
- 3. Sequence to Sequence Learning with Neural Networks論文翻譯
- 4. How to Implement Neural Networks with TensorFlow
- 5. 【論文翻譯】End-To-End Memory Networks
- 6. Image-to-Image Translation with Conditional Adversarial Networks 論文翻譯
- 7. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯
- 8. 【論文翻譯】Fully Convolutional Networks for Semantic Segmentation
- 9. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 10. 【論文翻譯】Fully Convolutional Networks for Semantic Segmentation_2
- 更多相關文章...
- • Eclipse 編譯項目 - Eclipse 教程
- • CAP理論是什麼？ - NoSQL教程
- • Github 簡明教程
- • Git可視化極簡易教程 — Git GUI使用方法
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文水記|How to Train Triplet Networks with 100K Identities?
- 2. (zhuan) How to Train Neural Networks With Backpropagation
- 3. Sequence to Sequence Learning with Neural Networks論文翻譯
- 4. How to Implement Neural Networks with TensorFlow
- 5. 【論文翻譯】End-To-End Memory Networks
- 6. Image-to-Image Translation with Conditional Adversarial Networks 論文翻譯
- 7. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯
- 8. 【論文翻譯】Fully Convolutional Networks for Semantic Segmentation
- 9. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 10. 【論文翻譯】Fully Convolutional Networks for Semantic Segmentation_2