Model interpretability and systematic, targeted model adaptation present central challenges in deep learning. In the domain of intuitive physics, we study the task of visually predicting stability of block towers with the goal of understanding and influencing the model's reasoning. Our contributions are two-fold. Firstly, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. Secondly, we deploy the stethoscope framework to provide an in-depth analysis of a state-of-the-art deep neural network for stability prediction, specifically examining its physical reasoning. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%. Intuitive physics in the deep learning community describes physical understanding acquired by neural networks in a data-driven as opposed to a rule-based manner: With an increasing amount of training examples, we expect an algorithm to develop a better understanding of its (physical) environment, especially when the task it is trained on is inherently linked to the physical rules governing the scene. However, what type of understanding the network develops highly depends on the types of scenarios it is confronted with and the task it is trying to solve. Furthermore, it depends on the network architecture, on regularisation techniques, on the training procedure, etc. As a result, in contrast to a rule-based approach, it is often hard to assess what form of physical understanding a neural network has developed. We are specifically interested in whether the network uses visual cues as shortcuts which reflect correlations in the dataset but are incommensurate with the underlying laws of physics the network was intended to learn.In this paper, we specifically focus on stability prediction of block towers, a task which has gained interest in both the deep learning BID10 BID22 BID8 and the robotics community in recent years BID11 b) . Images of towers of blocks stacked on top of each other are shown to a neural network. Its task is to predict whether the tower will fall over or not resulting in a binary classification problem. End-to-end learning approaches as well as simulation-based approaches achieve super-human performance on a real dataset BID10 BID22 BID8 . However, with investigation of trained deep learning models limited to occlusion-based attention analyses BID10 BID8 , it is not clear to what extent neural networks trained on this task take into account physical principles such as centre-of-mass or whether they follow visual cues instead. To this end, we introduce a variation of the ShapeStacks dataset presented by BID8 which facilitates the analysis of the effects of visual cues on the learning process. The stethoscope framework. The main network (blue), comprised of an encoder and a decoder, is trained for global stability prediction of block towers. The stethoscope (orange), a two layered perceptron, is trained to predict a nuisance parameter (local stability) where the input is Z, a learned feature from an arbitrary layer of the main network. The stethoscope loss is back-propagated with weighting factor λ to the main network. The value of λ determines whether the stethoscope operates in analytic (λ " 0), auxiliary (λ ą 0) or adversarial manner (λ ă 0).Motivated by the need for an effective tool to understand and guide the physical reasoning of the neural network and inspired by prior research in interpretability, multi-task learning and adversarial training, we present neural stethoscopes as a unified framework for the interrogation and perturbation of task-specific information at any layer. A stethoscope can be deployed in a purely analytic fashion whereby a question is posed via a stethoscope loss which is not propagated back into the main network. It can also be used to promote or suppress specific information by deploying either an auxiliary or an adversarial training mechanism. The concept is illustrated in FIG0 . We demonstrate that deploying an auxiliary stethoscope can be used to promote information conducive to the main task improving overall network performance. Conversely, we show that an adversarial stethoscope can mitigate a specific bias by effectively suppressing information. Moreover, the main network does not need to be changed in order to apply a neural stethoscope.In this work, we present two contributions: (1) An in-depth analysis of the state-of-the-art approach for intuitive stability prediction. To that end, we also introduce an extension to the existing ShapeStacks dataset which will be made publicly available. (2) A framework for interpreting, suppressing or promoting extraction of features specific to a secondary task unifying existing approaches from interpretability, auxiliary and adversarial learning. While we frame this work in the context of intuitive physics, questions regarding model interpretability and, consequently, systematic, targeted model adaptation find applicability in all domains of deep learning. For a study of two MNIST toy problems with neural stethoscopes, please see Appendix C. We study the state-of-the-art approach for stability prediction of block towers and test its physical understanding. To that end, we create a new dataset and introduce the framework of neural stethoscopes unifying multiple threads of work in machine learning related to analytic, auxiliary and adversarial probing of neural networks. The analytic application of stethoscopes allows measuring relationships between different prediction tasks. We show that the network trained on stability prediction also obtains a more fine-grained physical understanding of the scene (origin of instability) but at the same time is susceptible to potentially misleading visual cues (i.e., local stability). In addition to the analysis, the auxiliary and adversarial modes of the stethoscopes are used to support beneficial complementary information (origin of instability) and suppress harmful nuisance information (visual cues) without changing the network architecture of the main predictor. This yields substantial performance gains in unfavourable training conditions where data is biased or labels are partially unavailable. We encourage the use of neural stethoscopes for other application scenarios in the future as a general tool to analyse task relationships and suppress or promote extraction of specific features. This can be done by collecting additional labels or using existing multi-modal data.
Combining auxiliary and adversarial training to interrogate and help physical understanding.
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