Feature Visualization

Feature visualization aims to understand the internal representations learned by complex models, such as deep neural networks, by visualizing the features they activate. Current research focuses on improving the interpretability and robustness of these visualizations, exploring techniques like activation maximization, gradient-based methods, and novel optimization strategies to generate more natural and reliable representations of learned features. This work is crucial for enhancing the transparency and trustworthiness of machine learning models across diverse applications, from image classification and neuroimaging analysis to smart energy management, by providing insights into model decision-making processes and identifying potential biases.