Thispaper presents anefficient multi-scale vision Transformer,called ResT,that capably served as a general-purpose backbone for image recognition.

There exists a stream of papers in the literature on the regularization effect of Dropout for linear models[Wageretal.,2013,MianjyandArora,2019,HelmboldandLong,2015,Cavazzaetal.,2017].

By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.

By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.

Motivated bythesuccess ofdiffusion models inhigh-fidelity and photorealistic image generation, generative data augmentation is an emerging application of diffusion models.