4 papers · 1 filter
Hierarchical, Interpretable, Label-Free Concept Bottleneck Model
Haodong Xie, Yujun Cai, Rahul Singh Maharjan +3
Concept Bottleneck Models (CBMs) introduce interpretability to black-box deep learning models by predicting labels through human-understandable concepts. However, unlike humans, wh…
Representation Understanding via Activation Maximization
Hongbo Zhu, Angelo Cangelosi
Understanding internal feature representations of deep neural networks (DNNs) is a fundamental step toward model interpretability. Inspired by neuroscience methods that probe biolo…
Attributes-aware Visual Emotion Representation Learning
Rahul Singh Maharjan, Marta Romeo, Angelo Cangelosi
Visual emotion analysis or recognition has gained considerable attention due to the growing interest in understanding how images can convey rich semantics and evoke emotions in hum…
Noise-Free Explanation for Driving Action Prediction
Hongbo Zhu, Theodor Wulff, Rahul Singh Maharjan +2
Although attention mechanisms have achieved considerable progress in Transformer-based architectures across various Artificial Intelligence (AI) domains, their inner workings remai…