Title: Novel Class Discovery under Unreliable Sampling. Abstract: When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is also known as novel class discovery (NCD). However, sampling errors are common in practice and may make the NCD process unreliable. To tackle this problem, this paper introduces a new and more realistic setting, where collectors may misidentify known classes and even confuse known classes with novel classes - we name it NCD under unreliable sampling (NUSA). We find that NUSA will empirically degrade existing NCD methods if taking no care of sampling errors. To handle NUSA, we propose an effective solution, named hidden-prototype-based discovery network (HPDN). HPDN first trains a deep network to fully fit the wrongly sampled data, then applies the relatively clean hidden representations yielded by this network into a novel mini-batch K-means algorithm, which further prevents them overfitting to residual errors by detaching noisy supervision timely. Experiments demonstrate that, under NUSA, HPDN significantly outperforms competitive baselines (e.g., 6% more than the best baseline on CIFAR-10) and keeps robust even encountering serious sampling errors.