Surprisingly, we show that even the addition ofnon-realistic random data (generated byGaussian sampling) can improverobustness.

We introduce quantization with incoherence processing (QuIP), a new method based on the insight that quantization benefits from incoherent weight and Hessian matrices, i.e., from the weights being even in magnitude and the

Image super-resolution (SR) aims to reconstruct high-resolution (HR) images with more details from low-resolution (LR) images. Recently, deep learning-based image SR methods have made significant progress inreconstruction performance through deeper networkmodels andlarge-scale training datasets, but these improvements place higher demands on both computing power and memory resources, thus requiring more efficient solutions.