fig3
![Unsupervised monocular depth estimation with aggregating image features and wavelet SSIM (Structural SIMilarity) loss](https://image.oaes.cc/04b5dce2-3cc7-4f49-a7bd-bb9feb988e17/4326.fig.3.jpg)
Figure 3. The architecture of ResNet and ResNeXt block: (a) the ResNet block; and (b) the aggregated residual transformations. Both have similar complexity, but the ResNeXt block has better adaptability and expansibility.
Figure 3. The architecture of ResNet and ResNeXt block: (a) the ResNet block; and (b) the aggregated residual transformations. Both have similar complexity, but the ResNeXt block has better adaptability and expansibility.
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