XAI (eXplainable AI) has become a pivotal area of research with the advancement of deep learning (DL) technologies and applications. Post-hoc explanation methods interpret deep learning predictions by uncovering the significance of input features, while visualization tools can contribute to a deep understanding of AI model reasoning based on these methods. In this paper, we survey a broad spectrum of post-hoc explanation methods and the visual analytics work based on them. First, we categorize the computational methods into four main types: perturbation-based, gradient-based, decomposition-based, and concept-based. While the first three focus on attributing the model’s output to specific regions of the input image, concept-based methods provide global explanations by mapping human-understandable concepts to high-level features. Then, we examine the methodologies, features, strengths, and limitations of each approach. Moreover, we review existing visualization-focused work based on these computational methods. Finally, we discuss further research challenges and opportunities for XAI visualization with post-hoc explanation.

Galaxy morphology classification is a crucial task in astronomy and astrophysics, providing information on galaxy formation and evolution. Traditionally, this classification has been a manual and labor-intensive process requiring significant astronomical expertise. However, advancements in artificial intelligence, particularly deep learning, offer more efficient and accurate solutions. We investigated the application of convolutional neural networks (CNNs) for galaxy morphology classification using the Galaxy10 DECals dataset. We developed and compared three models: a custom-built CNN using TensorFlow 2.18, a ResNet50 model initialized with random weights, and a pre-trained EfficientNetB5 model utilizing transfer learning, both implemented in PyTorch 2.6. Our results indicate that the custom model achieves an accuracy of 67%, while the ResNet50 and EfficientNetB5 models achieve 80 and 87% accuracy, respectively. The superior performance of the pre-trained Efficient-NetB5 model underscores the efficacy of transfer learning in astronomical image classification. These findings have significant implications for the application of deep learning techniques in astrophysical research.