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%.
This paper is about using "neural stethoscopes", small complementary neural networks that are added to a main network which with their auxilary loss functions can measure suitability of features or guide the learning process. The idea is incremental to multi-task learning and enable, in a single framework, to validate intermediate features for additional related tasks. Moreover it can promote or suppress the correlation of such features to the tasks related to the main one. The framework is applied to the task of visual stability prediction of block towers. The paper builds upon Groth et al. 2018, adding the concept of local stability as correlated secondary task, used with the proposed neural stethoscopes. Experiments with an extension of ShapeStacks (Groth et al. 2018) dataset where the local stability is added to the global stability class, show that it is possibile increase the performance using the additional task. Moreover, it is shown that neural stethoscopes can suppress nuisance information when using a biased training dataset where the local and global stability are purposely inversely correlated.
Strengths:
+ A very nice paper, well written and easy to read. Figures are helpful and the structure is clear.
+ The concept of neural stethoscope is interesting and simplify the concepts behind multitask learning.
+ Experiments are convincing, interesting and there is some novelty in vision stability prediction.
Weaknesses:
- The novelty is limited related to multitask learning, thus it is an incremental paper.