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Exploring Interpretability in Deep Learning for Affective Computing: A Comprehensive Review
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AI
DOI：10.1145/3723005.png)
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Abstract
En 中文
Deep learning has shown impressive performance in affective computing, but its black-box characteristic limits the model’s interpretability, posing a challenge to further development and application. Compared with objective recognition tasks such as image recognition, emotion perception as a high-level cognition is more subjective, making it particularly important to enhance the interpretability of deep learning in affective computing. In recent years, some interpretability-related works have emerged, but there are few reviews on this topic yet. This article summarizes the explainable deep learning methods in affective computing from two aspects: first, the application of general explainable deep learning methods in affective computing from the perspectives of model-agnostic and model-specific is introduced; second, emotion-specific interpretability research that combines emotional psychology theories, physiological studies, and human cognition, covering task design, model design, and result analysis methods, is systematically reviewed. There are new explainable deep learning methods for multimodal and large language models in the context of emotion. Finally, we discuss five specific challenges and propose corresponding future directions to provide insights and references for subsequent research on affective computing interpretability.
Keywords:
deep learning
affective computing
interpretability
explainable AI
emotion recognition
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