Convolution with even-sized kernels and symmetric paddingIntro本文探究了偶数kernel size的卷积对网络的影响,结果表明偶数卷积在结果上并不如奇数卷积。文章从实验与原理上得出结论,偶数卷积之所以结果更差,是因为偶数卷积会使得feature map偏移,即“the shift problem”,这将导致学习到的feature不具备更强的表征能力。
Besides, 3×3 kernels dominate the spatial representation in these models, whereas even-sized kernels (2×2, 4×4) are rarely adopted. In this work, we quantify the shift problem occurs in even-sized kernel convolutions by an information erosion hypothesis, and eliminate it by proposing ...
However, even though this can be done by increasing the kernel size, it would result in an increase in the number of parameters. A more appropriate way is to reduce the size of the feature map and use the same sized kernel as before, but it can span over a larger area corresponding ...
By comparison, the involution kernel adopted in this work is generated condi- tioned on a single pixel, rather than its relationship with the neighboring pixels. To take one step further, we prove in our experiments that even with our embarrassingly simple vers...
Or do I misunderstand and you start with a Dataset that has a very sparse distribution of DGGS cells and you want true nearest-neighbor search (i.e., "dynamic-sized" convolution kernel)? Collaborator keewis Nov 13, 2023 • edited
We have added 1 in the output padding which will help in getting even sized output for comparison with the next output. Input: 4x4 | Filter Size: 4x4 | Strides: 4x4 | Padding: 0 | Output Padding: 0 Let’s check what will be the output size after the transposed convolution operation. ...
The kernel size of the 1D CNN in this study is equal to 3 that indicates the length of the 1D convolution window with stride size of 1. A Max-pooling layer with the window size equal to 2 is applied after the convolution layers to down-sample the features maps. The feature maps are ...
(MP). Conv1D employs multiple trainable convolutional kernels to capture different features, generating a feature map for each kernel. The BN layer is employed to stabilize and accelerate the training process, while the ELU layer enhances the model’s expressive capability. Additionally, the MP ...
RHi−1×Wi−1×Ci−1 as the input to stage i, we learn a function f (·) that maps xi−1 into new tokens f (xi−1) with a channel size Ci, where f (·) is 2D convolution operation of kernel size s × s, stride s − o and p padding (to deal with bounda...
The size of Maxpooling was the same as the size of the previous 1D convolution filter kernel. Drop- out dropped the feature representation of the previous layer with a 50% probability to reduce overfitting. Fig. 1 a Details of the SConv1D; b Schematic diagram and network structure; ...