  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.
