Comprehensible Convolutional Neural Networks via Guided Concept Learning
Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust. Post-hoc interpretation methods lack transparency in the feature representations learned by the models. This work proposes a guided learning approach with an additional concept layer in a CNN- based architecture to learn the associations between visual features and word phrases. We design an objective function that optimizes both prediction accuracy and semantics of the learned feature representations. Experiment results demonstrate that the proposed model can learn concepts that are consistent with human perception and their corresponding contributions to the model decision without compromising accuracy. Further, these learned concepts are transferable to new classes of objects that have similar concepts.
Code (1)
Similar Papers 제목 키워드 기반
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increa…
Explainable artificial intelligenceInterpretable Machine LearningKnowledge GraphsSurveyEnhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery
Concept-based explainable approaches have emerged as a promising method in explainable AI because they can interpret models in a way that aligns with human reasoning. However, their adaption in the text domain remains li…
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
Recent advancements in generative AI have introduced novel prospects and practical implementations. Especially diffusion models show their strength in generating diverse and, at the same time, realistic features, positio…
CoLAcounterfactualimage-classificationImage ClassificationComprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test
The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is how we can obtain an explanation on why…
Anomaly DetectionAstronomycounterfactualCounterfactual ExplanationRule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible since they use symbols that are semanticall…