Abstract: A method may include receiving a set of images, analyzing the images, selecting an internal layer, extracting neuron activations, factorizing the neuron activations via a matrix factorization algorithm to select prototypes and generate weights for each of the selected prototypes, replacing the neuron activations of the internal layer with the selected prototypes and the weights for the selected prototypes, receiving a second set of images, classifying the second set of images using the prototypes and weights, displaying the second set of images, selected prototypes, and weights, displaying predicted results and ground truth for the second set of images, providing error images based on the predicted results and ground truth; identifying error prototypes of the selected prototypes associated with the error images; ranking error weights of the error prototypes, and outputting a new image class based on the error prototypes being one of a top ranked error weights.