Section 5,Section 6andSection 8 explain more implementation details of the empirical implementation. We use "M" or "S" to distinguish contents inthemain fileorinthesupplementary file.

UOTA adaptively searches for the most important sampling region to produce views, and provides viable choice for outlier-robust self-supervised learning approaches.

There are several GAN literature that adopts multiple discriminators [9-14]. Among them, GMAN [10] is closely related to our approach in the sense that it utilizes an ensemble predictionofdiscriminators.

The objective function is the composition of two expectations of stochastic functions, and ismore challenging tooptimize than vanilla stochastic optimization problems.