  Large deep networks have demonstrated competitive performance in single image
super-resolution (SISR), with a huge volume of data involved. However, in
real-world scenarios, due to the limited accessible training pairs, large
models exhibit undesirable behaviors such as overfitting and memorization. To
suppress model overfitting and further enjoy the merits of large model
capacity, we thoroughly investigate generic approaches for supplying additional
training data pairs. In particular, we introduce a simple learning principle
MixUp to train networks on interpolations of sample pairs, which encourages
networks to support linear behavior in-between training samples. In addition,
we propose a data synthesis method with learned degradation, enabling models to
use extra high-quality images with higher content diversity. This strategy
proves to be successful in reducing biases of data. By combining these
components -- MixUp and synthetic training data, large models can be trained
without overfitting under very limited data samples and achieve satisfactory
generalization performance. Our method won the second place in NTIRE2019 Real
SR Challenge.
