A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches

• URL: http://arxiv.org/abs/2504.04276v1
• Date: Sat, 05 Apr 2025 20:13:20 GMT
• Title: A Comparative Study of Explainable AI Methods: Model-Agnostic vs. Model-Specific Approaches
• Authors: Keerthi Devireddy,
• Abstract summary: I examine how LIME and SHAP differ from Grad-CAM and Guided Backpropagation when interpreting ResNet50 predictions.<n>I found that each method reveals different aspects of the models decision-making process.<n>My analysis shows there is no "one-size-fits-all" solution for model interpretability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper compares model-agnostic and model-specific approaches to explainable AI (XAI) in deep learning image classification. I examine how LIME and SHAP (model-agnostic methods) differ from Grad-CAM and Guided Backpropagation (model-specific methods) when interpreting ResNet50 predictions across diverse image categories. Through extensive testing with various species from dogs and birds to insects I found that each method reveals different aspects of the models decision-making process. Model-agnostic techniques provide broader feature attribution that works across different architectures, while model-specific approaches excel at highlighting precise activation regions with greater computational efficiency. My analysis shows there is no "one-size-fits-all" solution for model interpretability. Instead, combining multiple XAI methods offers the most comprehensive understanding of complex models particularly valuable in high-stakes domains like healthcare, autonomous vehicles, and financial services where transparency is crucial. This comparative framework provides practical guidance for selecting appropriate interpretability techniques based on specific application needs and computational constraints.

Related papers

• Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks [5.081175754775484]
  We introduce XDELTA, a novel explainable AI tool that explains differences between a high-accuracy base model and a computationally efficient but lower-accuracy edge model. We conduct a comprehensive evaluation to test XDELTA's ability to explain model discrepancies, using over 1.2 million images and 24 models, and assessing real-world deployments with six participants.
  arXiv  Detail & Related papers  (2024-07-13T22:05:58Z)
• COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification [21.3855970055692]
  We present a set of metrics that utilize vision priors to assess the performance of saliency methods on image classification tasks. We show that although saliency methods are thought to be architecture-independent, most methods could better explain transformer-based models over convolutional-based models.
  arXiv  Detail & Related papers  (2023-09-20T01:06:44Z)
• Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image Classification [0.4532517021515834]
  We leverage state-of-the-art machine learning approaches to perform remote sensing image classification. We offer insights and recommendations for selecting the most appropriate XAI method.
  arXiv  Detail & Related papers  (2023-06-06T22:04:45Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models [2.741266294612776]
  We propose a model-agnostic method for generating saliency maps that has access only to the output of the model. We use Differential Evolution to identify which image pixels are the most influential in a model's decision-making process.
  arXiv  Detail & Related papers  (2022-09-19T10:28:37Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.