Why do deep convolutional networks generalize so poorly to small image transformations?
CVPR2018 論文地址：https://arxiv.org/abs/1805.12177 論文主要內容：研究爲什麼現代流行的深度學習模型（如VGG16，ResNet50，InceptionResNetV2）對微小的圖像變換（包括尺度，平移, 幾個像素改變）不具有不變性，即網絡的泛化能力不好。 作者結論：這種不變性與深度網絡中的subsampling操作及數據集存在的bias有關
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相關文章
- 1. Why do deep convolutional networks generalize so poorly to small image transformations?論文筆記
- 2. [深度學習論文筆記][arxiv 1805] Why do deep convolutional networks generalize so poorly to small image transf
- 3. Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
- 4. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 5. A Beginner's Guide To Understanding Convolutional Neural Networks - Part 2
- 6. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 7. Geoffrey Hinton's Capsule Networks: A Novel Approach to Deep Learning
- 8. why deep learning works
- 9. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
- 10. 【轉】A Beginner's Guide To Understanding Convolutional Neural Networks