TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
因爲老闆之後可能要做深度學習框架的模糊測試，所以把之前一直沒啃的paper先啃了。。 摘要 本文開發一個對神經網絡的 coverage-guided fuzzing(CGF) 方法。然後討論應用CGF達到以下目標：找到預訓練好的神經網絡中的數值型錯誤，生成神經網絡的不一致並找到這些版本的神經網絡，發現字符級別語言模型中意想不到的行爲。最後開源了這個工具。 1. 緒論 機器學習模型由於下述原因很難d
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相關文章
- 1. [論文解讀]TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
- 2. Few-shot Learning with Graph Neural Networks
- 3. Brain tumor segmentation with Deep Neural Networks
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. Convolutional Neural Networks with Alternately Updated Clique
- 6. Few-Shot Learning with Graph Neural Networks
- 7. Overlapping Community Detection with Graph Neural Networks
- 8. How to Implement Neural Networks with TensorFlow
- 9. Balanced Binary Neural Networks With Gated Residual
- 10. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks