Negative Results on Negative Images: Major Flaw in Deep Learning?

DNNs, which are merely trained on raw data, do not recognize the semantics of the objects, but rather memorize the inputs. The short 3 page paper, titled "Deep Neural Networks Do Not Recognize Negative Images," provides support to its title via experimentation using a "state-of-the-art" deep (convolutional) neural network, which is separately trained on both MNIST and the German Traffic Signs Recognition Benchmark (GTSRB) datasets. The "regular" results of the MNIST and GTSRB recognition and classification reach greater than 99% and 98% accuracy on test data, respectively, while testing with the same negative images result in between 4% and 17% and between 5% and 14% accuracy, respectively. To assess the behavior of the image classification models, we evaluate the performance of DNNs on negative images of the training data. A negative is referred to an image with reversed brightness, i.e., the lightest parts appear the darkest and the darkest parts appear lightest.