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 ...
针对深度级别/可分离的卷积,可以使用卷积组参数,如果groups = nInputPlane,就是Depthwise;如果groups = nInputPlane,kernel=(K, 1)(针对二维卷积,前面加上,groups=1 and kernel=(1, K)),就是可分离的。 以下将torch的官方手册搬过来介绍使用方法(https://pytorch.org/docs/master/n... ...
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
Essentially, even if we just want to compute it modulo some number mm, the best thing we can do is to evaluate Rademacher formula with enough precision to get the exact value as an integer, and then compute it modulo mm. I'm not sure I understand all of the details of the algorithm ...
(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 ...
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 ver...
Next, a Convolutional Projection is implemented using a depth-wise separable convolution layer with kernel size s. Finally, the projected tokens are flattened into 1D for subsequent process. This can be formulated as: xiq/k/v=Flatten(Conv2d(Reshape2D(xi),s)), (2) where xiq/k...
. However I came up with a routine that seems to achive the task. Note that there are a bunch of tricks to handle kernel sizes that can be even or odd. The flipping of the kernel is meant to adapt my routine with the conv2 routine of Matlab (and for the same reasonone might...
As for SGC with fixed pattern partition, we find it learned an irregular convolutional kernel. In Fig.6a, we show a simple case in a 2D convolution which divides voxels into two groups. The valid convolutional kernel shape is always “X” because the sparsity pattern keeps the same when sli...