论文阅读笔记（四十四）：Deconvolutional Networks
Building robust low and mid-level image representations, beyond edge primitives, is a long-standing goal in vision. Many existing feature detectors spatially pool edge information which destroys cues
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相关文章
- 1. 《PIXEL DECONVOLUTIONAL NETWORKS》论文阅读笔记
- 2. 论文阅读笔记（四十三）：Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
- 3. 论文阅读笔记（四十五）：Deformable Convolutional Networks
- 4. 论文阅读笔记（四十二）：Visualizing and Understanding Convolutional Networks
- 5. 论文阅读笔记（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）
- 6. 论文阅读笔记（四十七）：Attention Is All You Need
- 7. 论文阅读笔记（四十六）：Generative Adversarial Nets
- 8. DRN：Dilated Residual Networks（论文解读十四）
- 9. 论文阅读笔记（四十一）：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 10. 论文阅读笔记（五十四）：V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation