File:Resnet He 2015 Fig3.png
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Figure 3. Example network architectures for ImageNet. Left: the VGG-19 model(19.6 billion FLOPs) as a reference. Middle: a plain network with 34 parameter layers (3.6 billion FLOPs). Right: a residual network with 34 parameter layers (3.6 billion FLOPs). The dotted shortcuts increase dimensions. Table 1 shows more details and other variants. In: Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun (2015). "Deep residual learning for image recognition". CoRR, vol. abs/1512.03385.
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 23:51, 30 September 2018 | | 1,111 × 479 (164 KB) | Omoreira (talk | contribs) | <P>Figure 3. Example network architectures for ImageNet. Left: the VGG-19 model(19.6 billion FLOPs) as a reference. Middle: a plain network with 34 parameter layers (3.6 billion FLOPs). Right: a residual network with 34 parameter layers... |
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