We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training. This allows the  DNN to abstain on confusing or difficult-to-learn examples while improving performance on the non-abstained samples. We show that such deep abstaining classifiers can: (i) learn representations for structured noise -- where noisy training labels or confusing examples are correlated with underlying features -- and then learn to abstain based on such features; (ii) enable robust learning in the presence of arbitrary or unstructured noise by identifying noisy samples; and (iii) be used as an effective out-of-category detector that learns to reliably abstain when presented with samples from  unknown classes. We provide analytical results on loss function behavior that enable automatic tuning of accuracy and coverage, and demonstrate the utility of the deep abstaining classifier using multiple image benchmarks, Results indicate significant improvement in learning in the presence of label noise.
A deep abstaining neural network trained with a novel loss function that learns representations for when to abstain enabling robust learning in the presence of different types of noise. A new loss function for training a deep neural network which can abstain, with performance looked at from angles in existence of structured noise, in existence of unstructured noise, and open world detection. This manuscript introduces deep abstaining classifiers which modifies the multiclass cross-entropy loss with an abstention loss, which is then applied to perturbed image classification tasks