Self-training with Noisy Student improves ImageNet classification閱讀筆記
論文地址：https://arxiv.org/pdf/1911.04252v1.pdf 這篇文章實現了自訓練方法，使用部分帶標籤數據進行訓練，在ImageNet上top-1爲87.4%。 方法： 1. 爲有標籤和無標籤的圖片。 2. 使用有標籤的數據、標準交叉熵損失訓練了一個EfficientNet作爲教師網絡。 3. 用這個教師網絡，在無標籤數據上生成僞標籤，僞標籤可以是soft label(持
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相關文章
- 1. Self-training with Noisy Student improves ImageNet classification論文解析
- 2. Self-training with Noisy Student
- 3. ImageNet Classification With Deep Convolutional Neural Networks閱讀筆記
- 4. ImageNet Classification with Deep Convolutional Neural Networks-AlexNet閱讀筆記
- 5. ImageNet classification with deep convolutional neural networks閱讀筆記
- 6. Reinforcement Learning for Relation Classification from Noisy Data閱讀筆記
- 7. alexnet筆記（ImageNet Classification with Deep Convolutional Neural Networks）
- 8. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 9. ImageNet Classification with Deep Convolutional Neural Network讀書筆記
- 10. ImageNet Classification with Deep Convolutional Neural Networks -- 解讀