Abstract. A new class of predictors, denoted realistic predictors, is de- fined. These are predictors that, like humans, assess the difficulty of examples, reject to work on those that are deemed too hard, but guarantee good performance on the ones they operate on. In this paper, we talk about a particular case of it, realistic classifiers. The central problem in realistic classification, the design of an inductive predictor of hardness scores, is considered. It is argued that this should be a predictor independent of the classifier itself, but tuned to it, and learned without explicit supervision, so as to learn from its mistakes. A new architecture is proposed to accomplish these goals by complementing the classifier with an auxiliary hardness prediction network (HP-Net). Sharing the same inputs as classifiers, the HP-Net outputs the hardness scores to be fed to the classifier as loss weights. Alternatively, the output of classifiers is also fed to HP-Net in a new defined loss, variant of cross entropy loss. The two networks are trained jointly in an adversarial way where, as the classifier learns to improve its predictions, the HP-Net refines its hardness scores. Given the learned hardness predictor, a simple implementation of realistic classifiers is proposed by rejecting examples with large scores. Experimental results not only provide evidence in support of the effectiveness of the proposed architecture and the learned hardness predictor, but also show that the realistic classifier always improves performance on the examples that it accepts to classify, performing better on these examples than an equivalent nonrealistic classifier. All of these make it possible for realistic classifiers to guarantee a good performance.