論文閱讀筆記《A semi-supervised CNN based method for steel surface defect recognition》
核心思想 本文提出一種半監督的鋼鐵表面缺陷檢測方法（PLCNN），半監督的思路也比較常見，利用CNN對無標籤樣本進行預測，輸出僞標籤（Pseudo-Label），並將帶有僞標籤的樣本作爲訓練樣本對網絡進行進一步訓練。 網絡結構的設計也沒有什麼特別之處，唯一有點新意的地方可能就是損失函數的設計了。 實現過程 網絡結構 損失函數 損失函數包含有標籤損失和無標籤損失兩個部分，兩個部分均
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相關文章
- 1. 論文閱讀筆記《A Surface Defect Detection Method Based on Positive Samples》
- 2. 論文閱讀筆記《A new graph-based semi-supervised method for surface defect classification》
- 3. 論文閱讀筆記《Segmentation-based deep-learning approach for surface-defect detection》
- 4. 論文閱讀筆記《Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo》
- 5. 論文閱讀- Segmentation-Based Deep-Learning Approach for Surface-Defect Detection
- 6. 論文閱讀筆記《Surface defect classification of steels with a new semi-supervised learning method》
- 7. A Surface Defect Detection Method Based on Positive Samples
- 8. 論文閱讀筆記《A weak supervision machine vision detection method based on artificial defect simulation》
- 9. 論文閱讀筆記《Unsupervised fabric defect detection based on a deep convolutional GAN》
- 10. 論文閱讀筆記《A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection》