Learning Interpretable and Influential Directions with Signal Vectors and Uncertainty Region Alignment
Keywords: latent space, interpretability, concepts, directions, signals, patterns, distractors
TL;DR: The proposed unsupervised method identifies a pair of latent space directions (filter and signal) with the first being able to answer questions of interpretability and the second to answer questions of concept influence on model's predictions
Abstract: Latent space directions have played a key role in understanding, debugging, and fixing deep learning models. Concepts are often encoded in distinct feature space directions, and evaluating impact of these directions on the model's predictions, highlights their importance in the decision-making process. Additionally, recent studies have shown that penalizing directions associated with spurious artifacts during training can force models to unlearn features irrelevant to their prediction task. Identifying these directions, therefore, provides numerous benefits, including a deeper understanding of the model's strategy, fostering trust, and enabling model correction and improvement. We introduce a novel unsupervised approach utilizing signal vectors and uncertainty region alignment to discover latent space directions that meet two key debugging criteria: significant influence on model predictions and high level of interpretability. To our knowledge, this method is the first of its kind to uncover such directions, leveraging the inherent structure of the feature space and the knowledge encoded in the deep network. We validate our approach using both synthetic and real-world benchmarks, demonstrating that the discovered directions effectively fulfill the critical debugging criteria.
Primary Area: interpretability and explainable AI
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Submission Number: 4691
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