DNN’s unsupervised feature extraction alleviates issues with feature engineering, greatly improves accuracy for Nudity detection (a relatively complex image classification problem) in vRate.
In a recent post, Ran Bi (NYU) discusses how DNN is prone to ‘missing the big picture’ by focusing on what it recognizes as a feature from familiar images which it has been trained on. His argument is based on a study by researchers from University of Wyoming and Cornell University who generated images completely unrecognizable to human eyes while getting DNN to still label them as familiar objects (such as cheetah/peacock/baseball/…) with 99.99% confidence. An interesting read with a precautionary quote:
“This may be another evidence to prove that Deep Neural Network does not see the world in the same way as human vision. We use the whole image to identify things while DNN depends on the features.”
Extending vRate’s approach to successfully detect nudity in images leads to solving problems involving broader classification and rating of media content when scaling an effective use of DNN cooperation with other classifiers.