  We introduce Differentiable Reasoning (DR), a novel semi-supervised learning
technique which uses relational background knowledge to benefit from unlabeled
data. We apply it to the Semantic Image Interpretation (SII) task and show that
background knowledge provides significant improvement. We find that there is a
strong but interesting imbalance between the contributions of updates from
Modus Ponens (MP) and its logical equivalent Modus Tollens (MT) to the learning
process, suggesting that our approach is very sensitive to a phenomenon called
the Raven Paradox. We propose a solution to overcome this situation.
