IMPACTX: Improving Model Performance by Appropriately predicting CorrecT eXplanations

• URL: http://arxiv.org/abs/2502.12222v1
• Date: Mon, 17 Feb 2025 14:15:20 GMT
• Title: IMPACTX: Improving Model Performance by Appropriately predicting CorrecT eXplanations
• Authors: Andrea Apicella, Salvatore Giugliano, Francesco Isgrò, Roberto Prevete,
• Abstract summary: IMPACTX is a novel approach that leverages XAI as a fully automated attention mechanism. It provides proper feature attribution maps for the model's decisions, without relying on external XAI methods.
• Score: 0.0
• License:
• Abstract: The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However, there is a growing interest in using XAI techniques to automatically improve the performance of the AI systems themselves. This paper proposes IMPACTX, a novel approach that leverages XAI as a fully automated attention mechanism, without requiring external knowledge or human feedback. Experimental results show that IMPACTX has improved performance respect to the standalone ML model by integrating an attention mechanism based an XAI method outputs during the model training. Furthermore, IMPACTX directly provides proper feature attribution maps for the model's decisions, without relying on external XAI methods during the inference process. Our proposal is evaluated using three widely recognized DL models (EfficientNet-B2, MobileNet, and LeNet-5) along with three standard image datasets: CIFAR-10, CIFAR-100, and STL-10. The results show that IMPACTX consistently improves the performance of all the inspected DL models across all evaluated datasets, and it directly provides appropriate explanations for its responses.

Related papers

• Explainable Artificial Intelligence for Dependent Features: Additive Effects of Collinearity [0.0]
  We propose an Additive Effects of Collinearity (AEC) as a novel XAI method to consider the collinearity issue. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method.
  arXiv  Detail & Related papers  (2024-10-30T07:00:30Z)
• SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios [1.2172320168050466]
  We propose a novel Explainable Active Learning model, XAL-based semantic segmentation model "SegXAL" SegXAL can (i) effectively utilize the unlabeled data, (ii) facilitate the "Human-in-the-loop" paradigm, and (iii) augment the model decisions in an interpretable way. In particular, we investigate the application of the SegXAL model for semantic segmentation in driving scene scenarios.
  arXiv  Detail & Related papers  (2024-08-08T14:19:11Z)
• Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
  Support of artificial intelligence based decision-making is a key element in future 6G networks. In such applications, using AI as black-box models is risky and challenging. We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
  arXiv  Detail & Related papers  (2024-07-09T16:24:21Z)
• Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
  AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The mapping between context and AI model parameters is ideally done in a zero-shot fashion. This paper introduces a general methodology for the online optimization of AMS mappings.
  arXiv  Detail & Related papers  (2024-06-22T11:17:50Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• Strategies to exploit XAI to improve classification systems [0.0]
  XAI aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. Most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system.
  arXiv  Detail & Related papers  (2023-06-09T10:38:26Z)
• AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models [1.8752655643513647]
  XAI tools can increase the vulnerability of model extraction attacks, which is a concern when model owners prefer black-box access. We propose a novel retraining (learning) based model extraction attack framework against interpretable models under black-box settings. We show that AUTOLYCUS is highly effective, requiring significantly fewer queries compared to state-of-the-art attacks.
  arXiv  Detail & Related papers  (2023-02-04T13:23:39Z)
• Optimizing Explanations by Network Canonization and Hyperparameter Search [74.76732413972005]
  Rule-based and modified backpropagation XAI approaches often face challenges when being applied to modern model architectures. Model canonization is the process of re-structuring the model to disregard problematic components without changing the underlying function. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures.
  arXiv  Detail & Related papers  (2022-11-30T17:17:55Z)
• 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)

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.