I want to train a deep neural network to classify images. In every implementation I have seen, multiclass training uses only the positive examples for each class.
Is there any way to utilize negative samples for the N image classes, without resorting to training N binary - classification networks?
By negative samples, I mean that all the images annotated with label x are negative samples for the class y (in the case when the class x is not a subset of y, and vice versa). We can use these negatives in binary classification, so is there a way to be used in a multiclass NN?