  Deep learning has led to a dramatic leap on Single Image Super-Resolution
(SISR) performances in recent years. %Despite the substantial advancement%
While most existing work assumes a simple and fixed degradation model (e.g.,
bicubic downsampling), the research of Blind SR seeks to improve model
generalization ability with unknown degradation. Recently, Kong et al pioneer
the investigation of a more suitable training strategy for Blind SR using
Dropout. Although such method indeed brings substantial generalization
improvements via mitigating overfitting, we argue that Dropout simultaneously
introduces undesirable side-effect that compromises model's capacity to
faithfully reconstruct fine details. We show both the theoretical and
experimental analyses in our paper, and furthermore, we present another easy
yet effective training strategy that enhances the generalization ability of the
model by simply modulating its first and second-order features statistics.
Experimental results have shown that our method could serve as a model-agnostic
regularization and outperforms Dropout on seven benchmark datasets including
both synthetic and real-world scenarios.
