Accurate Image Super-Resolution Using Very Deep Convolutional Networks筆記
摘要： （1）網絡結構深：a significant improvement in accuracy 通過級聯多層小的卷積核，上下文信息被高效利用 （2）深層網絡存在的問題是收斂速度 採用學習殘差和高的學習率 1.介紹 srcnn相對於傳統學習方法，不需要設計典型特徵，但是有3個問題：本文改善方法 （1）依賴於小區域的上下文信息（個人理解是由於網絡層少，視野比較小） 深層
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相關文章
- 1. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 2. Accurate Image Super-Resolution Using Very Deep Convolutional Networks閱讀筆記
- 3. 【論文筆記-VDSR】Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 4. 【超分辨率】VDSR--Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 5. 【論文閱讀】Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 6. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 7. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 8. 論文筆記：Very Deep Convolutional Networks for Large-Scale Image Recognition
- 9. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 10. VGG——Very deep convolutional networks for large-scale image recognition