Single domain generalization aims to train a generalizable model with only
one source domain to perform well on arbitrary unseen target domains. Image
augmentation based on Random Convolutions (RandConv), consisting of one
convolution layer randomly initialized for each mini-batch, enables the model
to learn generalizable visual representations by distorting local textures
despite its simple and lightweight structure. However, RandConv has structural
limitations in that the generated image easily loses semantics as the kernel
size increases, and lacks the inherent diversity of a single convolution
operation. To solve the problem, we propose a Progressive Random Convolution
(Pro-RandConv) method that recursively stacks random convolution layers with a
small kernel size instead of increasing the kernel size. This progressive
approach can not only mitigate semantic distortions by reducing the influence
of pixels away from the center in the theoretical receptive field, but also
create more effective virtual domains by gradually increasing the style
diversity. In addition, we develop a basic random convolution layer into a
random convolution block including deformable offsets and affine transformation
to support texture and contrast diversification, both of which are also
randomly initialized. Without complex generators or adversarial learning, we
demonstrate that our simple yet effective augmentation strategy outperforms
state-of-the-art methods on single domain generalization benchmarks.

研究提出了一种逐渐堆叠具有小卷积核的随机卷积层的渐进式方法来解决 RandConv 结构的局限性， 并将基本随机卷积层开发成一个包括可变形偏移和仿射变换的随机卷积块，以支持纹理和对比度的多样化。该策略在单一领域泛化基准测试中表现优异。

单域泛化的渐进式随机卷积

Progressive Random Convolutions for Single Domain Generalization

While successful for various computer vision tasks, deep neural networks have
shown to be vulnerable to texture style shifts and small perturbations to which
humans are robust. In this work, we show that the robustness of neural networks
can be greatly improved through the use of random convolutions as data
augmentation. Random convolutions are approximately shape-preserving and may
distort local textures. Intuitively, randomized convolutions create an infinite
number of new domains with similar global shapes but random local textures.
Therefore, we explore using outputs of multi-scale random convolutions as new
images or mixing them with the original images during training. When applying a
network trained with our approach to unseen domains, our method consistently
improves the performance on domain generalization benchmarks and is scalable to
ImageNet. In particular, in the challenging scenario of generalizing to the
sketch domain in PACS and to ImageNet-Sketch, our method outperforms
state-of-art methods by a large margin. More interestingly, our method can
benefit downstream tasks by providing a more robust pretrained visual
representation.

本文研究证明，数据增强中使用多尺度的随机卷积来增加数据集的多样性，可以显著提高神经网络的鲁棒性和泛化性能，特别是在图片领域的常见深度学习挑战中，描述如何将其应用于各种深度学习模型上。