【CVPR2020】Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a Ne
【CVPR2020】Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy 作者 摘要 數據增廣能有效提升深度網絡性能，本文對現有的超分數據增廣進行了一套綜合分析。對於圖像重建，像素空間關係很重要。本文提出CutBlur，即將高/低分辨率patch剪切到
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相關文章
- 1. A Comprehensive Analysis of Sequence Alignment Algorithms for LongRead Sequencing
- 2. data augmentation - image augmentation - imgaug
- 3. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
- 4. 【數據增強】綜述：A survey on Image Data Augmentation for Deep Learning
- 5. 【論文閱讀】Single Image Deraining: A Comprehensive Benchmark Analysis
- 6. A mixed-scale dense convolutional neural network for image analysis
- 7. （轉）AutoML for Data Augmentation
- 8. CutBlur-CVPR2020-專用於圖像復原的數據增廣策略 | Rethinking Data Augmentation for Image Super-resolution
- 9. data augmentation and dropout
- 10. [caption學習]：綜述：A comprehensive survey of deep learning for image caption