  Visually predicting the stability of block towers is a popular task in the
domain of intuitive physics. While previous work focusses on prediction
accuracy, a one-dimensional performance measure, we provide a broader analysis
of the learned physical understanding of the final model and how the learning
process can be guided. To this end, we introduce neural stethoscopes as a
general purpose framework for quantifying the degree of importance of specific
factors of influence in deep neural 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.
We apply neural stethoscopes to analyse the state-of-the-art neural network for
stability prediction. 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%.
