Title: PROTOTYPE-ASSISTED ADVERSARIAL LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION. Abstract: This paper presents a generic framework to tackle the crucial class mismatch problem in unsupervised domain adaptation (UDA) for multi-class distributions.  Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribution embedded in probabilistic predictions, hence bringing insufficient alleviation to the latent mismatch problem.  Compared with pseudo labels, class prototypes are more accurate and reliable since they summarize over all the instances and are  able  to  represent  the  inherent  semantic  distribution  shared  across  domains. Therefore, we propose a novel Prototype-Assisted Adversarial Learning (PAAL) scheme, which incorporates instance probabilistic predictions and class prototypes together  to  provide  reliable  indicators  for  adversarial  domain  alignment.   With the PAAL scheme,  we align both the instance feature representations and class prototype  representations  to  alleviate  the  mismatch  among  semantically  different classes.   Also,  we exploit the class prototypes as proxy to minimize the within-class variance in the target domain to mitigate the mismatch among semantically similar classes.  With these novelties, we constitute a Prototype-Assisted Conditional Domain Adaptation (PACDA) framework which well tackles the class mismatch problem. We demonstrate the good performance and generalization ability of the PAAL scheme and also PACDA framework on two UDA tasks, i.e., object recognition (Office-Home,ImageCLEF-DA, andOffice) and synthetic-to-real semantic segmentation (GTA5→CityscapesandSynthia→Cityscapes).