Enhancing Model Interpretability with Local Attribution over Global Exploration

• URL: http://arxiv.org/abs/2408.07736v1
• Date: Wed, 14 Aug 2024 17:53:08 GMT
• Title: Enhancing Model Interpretability with Local Attribution over Global Exploration
• Authors: Zhiyu Zhu, Zhibo Jin, Jiayu Zhang, Huaming Chen,
• Abstract summary: Current attribution algorithms evaluate the importance of each parameter by exploring the sample space. A large number of intermediate states are introduced during the exploration process, which may reach the model's Out-of-Distribution (OOD) space. We propose the Local Attribution (LA) algorithm that leverages these properties. Compared to the state-of-the-art attribution methods, our approach achieves an average improvement of 38.21% in attribution effectiveness.
• Score: 6.3144983055172235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of artificial intelligence, AI models are frequently described as `black boxes' due to the obscurity of their internal mechanisms. It has ignited research interest on model interpretability, especially in attribution methods that offers precise explanations of model decisions. Current attribution algorithms typically evaluate the importance of each parameter by exploring the sample space. A large number of intermediate states are introduced during the exploration process, which may reach the model's Out-of-Distribution (OOD) space. Such intermediate states will impact the attribution results, making it challenging to grasp the relative importance of features. In this paper, we firstly define the local space and its relevant properties, and we propose the Local Attribution (LA) algorithm that leverages these properties. The LA algorithm comprises both targeted and untargeted exploration phases, which are designed to effectively generate intermediate states for attribution that thoroughly encompass the local space. Compared to the state-of-the-art attribution methods, our approach achieves an average improvement of 38.21\% in attribution effectiveness. Extensive ablation studies in our experiments also validate the significance of each component in our algorithm. Our code is available at: https://github.com/LMBTough/LA/

Related papers

• GLEAMS: Bridging the Gap Between Local and Global Explanations [6.329021279685856]
  We propose GLEAMS, a novel method that partitions the input space and learns an interpretable model within each sub-region. We demonstrate GLEAMS' effectiveness on both synthetic and real-world data, highlighting its desirable properties and human-understandable insights.
  arXiv  Detail & Related papers  (2024-08-09T13:30:37Z)
• Prospector Heads: Generalized Feature Attribution for Large Models & Data [82.02696069543454]
  We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods. We demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data.
  arXiv  Detail & Related papers  (2024-02-18T23:01:28Z)
• Less is More: Fewer Interpretable Region via Submodular Subset Selection [54.07758302264416]
  This paper re-models the above image attribution problem as a submodular subset selection problem. We construct a novel submodular function to discover more accurate small interpretation regions. For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution.
  arXiv  Detail & Related papers  (2024-02-14T13:30:02Z)
• CPR++: Object Localization via Single Coarse Point Supervision [55.8671776333499]
  coarse point refinement (CPR) is first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. CPR++ can obtain scale information and further reduce the semantic variance in a global region.
  arXiv  Detail & Related papers  (2024-01-30T17:38:48Z)
• Coalescing Global and Local Information for Procedural Text Understanding [70.10291759879887]
  A complete procedural understanding solution should combine three core aspects: local and global views of the inputs, and global view of outputs. In this paper, we propose Coalescing Global and Local InformationCG, a new model that builds entity and time representations. Experiments on a popular procedural text understanding dataset show that our model achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-08-26T19:16:32Z)
• Provably Sample-Efficient RL with Side Information about Latent Dynamics [12.461789905893026]
  We study reinforcement learning in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space. We present an algorithm, called TASID, that learns a robust policy in the target domain, with sample complexity that is in the horizon.
  arXiv  Detail & Related papers  (2022-05-27T21:07:03Z)
• Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods [4.9449660544238085]
  The paper focuses on time series analysis and benchmark several state-of-the-art attribution methods. The presented experiments involve gradient-based and perturbation-based attribution methods. The findings accentuate that choosing the best-suited attribution method is strongly correlated with the desired use case.
  arXiv  Detail & Related papers  (2022-02-08T10:06:13Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)
• Scalable Approximate Inference and Some Applications [2.6541211006790983]
  In this thesis, we propose a new framework for approximate inference. Our proposed four algorithms are motivated by the recent computational progress of Stein's method. Results on simulated and real datasets indicate the statistical efficiency and wide applicability of our algorithm.
  arXiv  Detail & Related papers  (2020-03-07T04:33:27Z)

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.