Understanding the effective receptive field in deep convolutional neural networks
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Cites methods from "Understanding the effective recepti..."
...To apprehend the differences between the representations learned by rigid and deformable KPConv, we can compute its Effective Receptive Field (ERF) [21] at different locations....
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...We use Effective Receptive Field (ERF) [21] and ablation studies to compare rigid KPConv with deformable KPConv....
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...The ERF is a measure of the influence that each input point has on the result of a KPConv layer at a particular location....
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...We see that rigid KPConv ERF has a relatively consistent range on every type of object, whereas deformable KPConv ERF seems to adapt to the object size....
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...As we can see in Figure 7, the ERF varies depending on the object it is centered on....
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...To better understand the behavior of Deformable ConvNets, we visualize the spatial support of network nodes by their effective receptive fields [30], effective sampling locations, and error-bounded saliency regions....
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...The differences in these contributions are represented by an effective receptive field, whose values are calculated as the gradient of the node response with respect to intensity perturbations of each image pixel [30]....
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...Recent works on effective receptive fields [30] and salient regions [40, 41, 12, 6] reveal that only a small proportion of pixels in the theoretical receptive field contribute significantly to the final network prediction....
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Cites background from "Understanding the effective recepti..."
...Owing to the design of CNN structures, the receptive field of it is limited to local regions [47,27]....
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