Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed
variation of the dilated convolution in which the spacings between the non-zero
elements in the kernel, or equivalently their positions, are learnable.
Non-integer positions are handled via interpolation. Thanks to this trick,
positions have well-defined gradients. The original DCLS used bilinear
interpolation, and thus only considered the four nearest pixels. Yet here we
show that longer range interpolations, and in particular a Gaussian
interpolation, allow improving performance on ImageNet1k classification on two
state-of-the-art convolutional architectures (ConvNeXt and Conv\-Former),
without increasing the number of parameters. The method code is based on
PyTorch and is available at
this https URL

该论文提出了一种基于可学习空间的扩张卷积（DCLS）, 通过插值来处理非整数位置，其中使用了高斯插值，以提高在 ConvNeXt 和 Conv-Former 两种卷积网络上的 ImageNet1k 分类性能，而不增加模型参数。该方法基于 PyTorch 编写实现，并提供代码。

可学习空间扩展的空洞卷积：超越双线性插值

Dilated Convolution with Learnable Spacings: beyond bilinear  interpolation

Recent works indicate that convolutional neural networks (CNN) need large
receptive fields (RF) to compete with visual transformers and their attention
mechanism. In CNNs, RFs can simply be enlarged by increasing the convolution
kernel sizes. Yet the number of trainable parameters, which scales
quadratically with the kernel's size in the 2D case, rapidly becomes
prohibitive, and the training is notoriously difficult. This paper presents a
new method to increase the RF size without increasing the number of parameters.
The dilated convolution (DC) has already been proposed for the same purpose. DC
can be seen as a convolution with a kernel that contains only a few non-zero
elements placed on a regular grid. Here we present a new version of the DC in
which the spacings between the non-zero elements, or equivalently their
positions, are no longer fixed but learnable via backpropagation thanks to an
interpolation technique. We call this method "Dilated Convolution with
Learnable Spacings" (DCLS) and generalize it to the n-dimensional convolution
case. However, our main focus here will be on the 2D case. We first tried our
approach on ResNet50: we drop-in replaced the standard convolutions with DCLS
ones, which increased the accuracy of ImageNet1k classification at
iso-parameters, but at the expense of the throughput. Next, we used the recent
ConvNeXt state-of-the-art convolutional architecture and drop-in replaced the
depthwise convolutions with DCLS ones. This not only increased the accuracy of
ImageNet1k classification but also of typical downstream and robustness tasks,
again at iso-parameters but this time with negligible cost on throughput, as
ConvNeXt uses separable convolutions. Conversely, classic DC led to poor
performance with both ResNet50 and ConvNeXt. The code of the method is
available at:
this https URL

该研究提出了一种新型的 “可学习空隙扩张卷积”（DCLS）方法来增加卷积神经网络（CNN）的感受野大小，从而提高图像分类的准确性，而且不会增加可训练参数的数量。该方法可以通过在卷积核中只添加一些非零元素，然后通过反向传播技术学习它们的间距来实现。