W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

Since the milestone study by Alex Krizhevsky and colleagues in 2012 [1], Deep Learning (DL), with particular emphasis on Convolutional Neural Networks (CNNs), is the state-of-the-art method for image classification in many different applications. Besides classification performance, the reason for the success of CNNs is twofold: i) the recent boost of graphical processing units (GPUs) and parallel processing, that allows to train very large models; ii) the ever-growing availability of massive annotated task-specific datasets. Nonetheless, in many realistic applications many concerns may be raised about the reliability of such datasets both in terms of image and labelling quality, and consequently on the robustness of the CNN models trained and tested on them. As regards to image quality, standard CNNs are supposed to be fed with high quality samples. Nevertheless, in practical scenarios different kinds of image degradation can heavily affect the performance of a CNN both in the training and in the inference phase. Problems concerning image acquisition devices, poor image sensor, lighting conditions, focus, stabilization, exposure time or partial occlusion of the cameras may lead to produce low quality samples, which have been demonstrated to be one of the chief reasons for troublesome learning procedures of CNN models in many applications [2, 3, 4]. On the other hand, even though the CNN had been proficiently trained and validated on high quality data, noisy inputs can heavily affect the inference phase, and cause classification errors at run-time. A typical example are self-driving cars, where a partial occlusion of the image acquisition device may lead to misinterpret a road sign, with catastrophic consequences. In such settings, the well-known limitations of standard CNNs to broadcast information about how much the given input resembles the ones the model was trained on - and hence, whether the associated prediction should (or should not) be trusted - is also playing a major role.