[論文筆記]Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
作者：Anh Nguyen, Jason Yosinski, Jeff Clune 鏈接：https://arxiv.org/pdf/1412.1897.pdf 摘要： 本文的工作基於Christian Szegedy的Intriguing properties of neural networks一文，前文是利用箱約束下的L-BFGS算法來改造正確樣本，而本文能夠利用進化算法（梯度上升）的思想隨
相關文章
- 1. 對抗樣本論文學習：Deep Neural Networks are Easily Fooled
- 2. Understanding Neural Networks Through Deep Visualization
- 3. Understanding Neural Networks Through Deep Visualization 論文筆記
- 4. How transferable are features in deep neural networks? 論文筆記
- 5. 論文筆記：How transferable are features in deep neural networks?
- 6. 論文閱讀筆記（五十六）：Image Super-Resolution Using Deep Convolutional Networks
- 7. Deep Neural Networks for Object Detection
- 8. 論文筆記——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 9. Deep Neural Networks for Object Detection論文翻譯
- 10. [論文解讀] Concolic Testing for Deep Neural Networks
- 更多相關文章...
- • Docker images 命令 - Docker命令大全
- • ASP.NET Razor - 標記 - ASP.NET 教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • Scala 中文亂碼解決
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. 對抗樣本論文學習：Deep Neural Networks are Easily Fooled
- 2. Understanding Neural Networks Through Deep Visualization
- 3. Understanding Neural Networks Through Deep Visualization 論文筆記
- 4. How transferable are features in deep neural networks? 論文筆記
- 5. 論文筆記：How transferable are features in deep neural networks?
- 6. 論文閱讀筆記（五十六）：Image Super-Resolution Using Deep Convolutional Networks
- 7. Deep Neural Networks for Object Detection
- 8. 論文筆記——Aggregated Residual Transformations for Deep Neural Networks(ResNeXt)
- 9. Deep Neural Networks for Object Detection論文翻譯
- 10. [論文解讀] Concolic Testing for Deep Neural Networks