Emotional regulation improves deep learning-based image classification
Emotion significantly influences cognition, enhancing memory and learning under certain conditions. Drawing on this principle, emotion-augmented deep learning investigates how affective states can improve neural network architectures and learning paradigms, achieving better generalization than non-emotional models. However, existing methods often rely solely on objective neurophysiological factors, neglecting the role of subjectivity in emotion. To bridge this gap, the present study introduces Emotional Regulation, a novel framework for modeling emotion in deep learning through artificial subjective experience. The method employs pre-training based on affective stimuli, balancing non-emot...
| Authors | Riccardo Emanuele Landi, João M. F. Rodrigues, Marta Chinnici |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 1 September 2026 |
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