Abstract / truncated to 115 words (read the full abstract)

The objective of the dissertation is to rethink the inductive nature of reasoning in neural networks by providing contextual explanations to a network’s decision and addressing the network's robustness capabilities. Neural networks represent data as projections on trained weights in a high dimensional manifold. The trained weights act as a knowledge base consisting of causal class dependencies. Inference built on features that identify dependencies within this manifold is termed as inductive feed-forward inference. This is a classical cause-to-effect inference model that is widely used because of its simple mathematical functionality and ease of operation. Nevertheless, feed-forward models do not generalize well to untrained situations. To alleviate this generalization challenge, we use an effect-to-cause inference model ... toggle 5 keywords

explainable machine learning – abductive reasoning – contrastive explanations – robust image recognition – image quality assessment

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