【論文快讀】Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
鏈接：https://arxiv.org/abs/1406.4729 作者：Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun 摘要： SPP可以用於: 1.大幅提高各種cnn的性能： 現有網絡對輸入圖片的size要求一定，這種「人工設定」可能對accuracy造成影響。本文提出的SPP-net則是「input size/scale free」的，輸
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- 1. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 2. [論文筆記]Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 3. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition論文翻譯
- 4. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition論文筆記
- 5. SPPNet：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 6. 《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》讀後感
- 7. 《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》閱讀筆記
- 8. 論文翻譯與解讀：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 9. 【SPP-net】《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》
- 10. 目標檢測--Spatial pyramid pooling in deep convolutional networks for visual recognition(PAMI, 2015)
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相關文章
- 1. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 2. [論文筆記]Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 3. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition論文翻譯
- 4. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition論文筆記
- 5. SPPNet：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 6. 《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》讀後感
- 7. 《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》閱讀筆記
- 8. 論文翻譯與解讀：Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- 9. 【SPP-net】《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》
- 10. 目標檢測--Spatial pyramid pooling in deep convolutional networks for visual recognition(PAMI, 2015)