Unlike machines, humans learn through rapid, Abstract model-building. The role of a teacher is not simply to hammer home right or wrong answers, but rather to provide intuitive comments, comparisons, and explanations to a pupil. This is what the Learning Under Privileged Information (LUPI) paradigm endeavors to model by utilizing extra knowledge only available during training. We propose a new LUPI algorithm specifically designed for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We propose to use a heteroscedastic dropout (i.e. dropout with a varying variance) and make the variance of the dropout a function of privileged information. Intuitively, this corresponds to using the privileged information to control the uncertainty of the model output. We perform experiments using CNNs and RNNs for the tasks of image classification and machine translation. Our method significantly increases the sample efficiency during learning, resulting in higher accuracy with a large margin when the number of training examples is limited. We also theoretically justify the gains in sample efficiency by providing a generalization error bound decreasing with O(1 n), where n is the number of training examples, in an oracle case.
Hoffman demonstrated a multi-modal distillation approach to incorporating an additional modality as side information @cite_13 . They start with a pre-trained network and distill the information from the privileged network to a main neural network in an end-to-end fashion.
@cite_13
2463402750
Several deep learning approaches have been applied to the autonomous driving task, many employing end-to-end deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This paper describes a technique for using Multi-Modal Multi-Task Learning that considers multiple behavioral modalities as distinct modes of operation for an end-to-end autonomous deep neural network utilizing the insertion of modal information as secondary input data. Using labeled data from hours of driving our fleet of 1 10th scale model cars, we trained multiple neural networks to imitate the steering angle and driving speed of human control of a car. We show that in each case, our models trained with MTL can match or outperform multiple networks trained on individual tasks, while using a fraction of the parameters and having more distinct modes of operation than a network trained without MTL on the same multi-modal data. These results should encourage Multi-Modal MTL-style training with the insertion of Modal Information for tasks with related behaviors.