【論文翻譯】Image Net Classification with Deep Convolutional Neural Networks
1.Introduction 目前的對象識別方法是機器學習方法的必要用途。爲了提高性能，我們可以收集更大的數據集，學習更強大的模型，並使用更好的技術來防止過度配置。直到最近，標記圖像的數據集相對較小 - 大約數萬個圖像（例如，NORB [16]，Caltech-101/256 [8,9]和CIFAR-10/100 [12]）。使用此大小的數據集可以很好地解決簡單的識別任務，特別是如果
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- 8. [論文翻譯]V-Net:Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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相關文章
- 1. ImageNet Classification with Deep Convolutional Neural Networks
- 2. AlexNet解析：ImageNet Classification with Deep Convolutional Neural Networks
- 3. ImageNet Classification with Deep Convolutional Neural Networks （AlexNet）翻譯
- 4. 論文翻譯：ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
- 5. 【論文翻譯】Deep Residual Learning for Image Recognition
- 6. 論文ImageNet Classification with Deep Convolutional Neural Networks（Alexnet2012）
- 7. VGGNet論文翻譯—Very Deep Convolutional Networks for Large-Scale Image Recognition
- 8. [論文翻譯]V-Net:Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
- 9. 【論文翻譯】Fully Convolutional Networks for Semantic Segmentation
- 10. ImageNet Classification with Deep Convolutional Neural Networks