An essential aspect of any machine learning system is understanding what the model does not know.

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the classifier during inference, allowing the classifier to reject an observation instance rather than predict an uncertain label. These rejected observations are translated by the generative network to high-certainty representations, which are then relabeled by the classifier. This architecture allows for any certainty-based classifier or rejection function and is not limited to multilayer perceptrons. The capability of this framework is assessed using benchmark classification datasets and shows that GWINs significantly improve the accuracy of uncertain observations.

An essential aspect of any machine learning system is understanding what the model does not know.

The method, using GANs to map low confidence examples to high confidence examples of the same class is highly original and shows promise as an effective method. The authors also provide a good description of how their work differs from Defense-GAN, and also notes some of the prior work on classifiers with a reject option (although they should also cite some of the more recent work doing so with DNNs e.g. The paper is clearly written, however there seems to be some information gaps with regards to network architecture and the mechanism for conditioning on the low confidence image x. Additionally there are several methods existing in the literature for conditioning the discriminator on label information: [1], [2], and concatenation and it's not clear from the paper which is used. The main drawback of this paper is that the datasets used (MNIST and FashionMNIST) are too toy to allow the reader to draw informed conclusions.

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the classifier during inference, allowing the classifier to reject an observation instance rather than predict an uncertain label. These rejected observations are translated by the generative network to high-certainty representations, which are then relabeled by the classifier. This architecture allows for any certainty-based classifier or rejection function and is not limited to multilayer perceptrons.

We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the classifier during inference, allowing the classifier to reject an observation instance rather than predict an uncertain label. These rejected observations are translated by the generative network to high-certainty representations, which are then relabeled by the classifier. This architecture allows for any certainty-based classifier or rejection function and is not limited to multilayer perceptrons.