Integrated Gradient Correlation: a Dataset-wise Attribution Method

• URL: http://arxiv.org/abs/2404.13910v2
• Date: Mon, 14 Jul 2025 08:06:34 GMT
• Title: Integrated Gradient Correlation: a Dataset-wise Attribution Method
• Authors: Pierre Lelièvre, Chien-Chung Chen,
• Abstract summary: We present a dataset-wise attribution method called Integrated Gradient Correlation (IGC)<n>IGC enables region-specific analysis by a direct summation over associated components, and further relates the sum of all attributions to a model prediction score (correlation)<n>We demonstrate IGC on synthetic data and fMRI neural signals (NSD dataset) with the study of the representation of image features in the brain.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attribution methods are primarily designed to study input component contributions to individual model predictions. However, some research applications require a summary of attribution patterns across the entire dataset to facilitate the interpretability of the scrutinized models at a task-level rather than an instance-level. It specifically applies when the localization of important input information is supposed to be stable for a specific problem but remains unidentified among numerous components. In this paper, we present a dataset-wise attribution method called Integrated Gradient Correlation (IGC) that enables region-specific analysis by a direct summation over associated components, and further relates the sum of all attributions to a model prediction score (correlation). We demonstrate IGC on synthetic data and fMRI neural signals (NSD dataset) with the study of the representation of image features in the brain and the estimation of the visual receptive field of neural populations. The resulting IGC attributions reveal selective patterns, coherent with respective model objectives.

Related papers

• Zero-Shot Image Anomaly Detection Using Generative Foundation Models [2.241618130319058]
  This research explores the use of score-based generative models as foundational tools for semantic anomaly detection.<n>By analyzing Stein score errors, we introduce a novel method for identifying anomalous samples without requiring re-training on each target dataset.<n>Our approach improves over state-of-the-art and relies on training a single model on one dataset -- CelebA -- which we find to be an effective base distribution.
  arXiv  Detail & Related papers  (2025-07-30T13:56:36Z)
• Information-theoretic Quantification of High-order Feature Effects in Classification Problems [0.19791587637442676]
  We present an information-theoretic extension of the High-order interactions for Feature importance (Hi-Fi) method.<n>Our framework decomposes feature contributions into unique, synergistic, and redundant components.<n>Results indicate that the proposed estimator accurately recovers theoretical and expected findings.
  arXiv  Detail & Related papers  (2025-07-06T11:50:30Z)
• Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies [0.757843972001219]
  We propose a systematic approach to examine the results of GNN models trained with structured and unstructured information from the MIMIC-III dataset. We show the robustness of this approach in identifying predictive features in PKGs for the task of readmission prediction.
  arXiv  Detail & Related papers  (2024-08-27T09:48:25Z)
• Ranking and Combining Latent Structured Predictive Scores without Labeled Data [2.5064967708371553]
  This paper introduces a novel structured unsupervised ensemble learning model (SUEL) It exploits the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery.
  arXiv  Detail & Related papers  (2024-08-14T20:14:42Z)
• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• Integrated feature analysis for deep learning interpretation and class activation maps [1.5943223374606597]
  We propose an integrated feature analysis method to look closer into the intermediate features extracted by deep learning models. This integrated feature analysis could provide information on overfitting, confounders, outliers in datasets. The method was evaluated by calculating the consistency between the CAMs average class activation levels and the logits of the model.
  arXiv  Detail & Related papers  (2024-07-01T10:10:57Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology [0.36096289461554343]
  We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology.
  arXiv  Detail & Related papers  (2024-03-22T14:32:02Z)
• The Importance of Downstream Networks in Digital Pathology Foundation Models [1.689369173057502]
  We evaluate seven feature extractor models across three different datasets with 162 different aggregation model configurations. We find that the performance of many current feature extractor models is notably similar.
  arXiv  Detail & Related papers  (2023-11-29T16:54:25Z)
• Generalizing Backpropagation for Gradient-Based Interpretability [103.2998254573497]
  We show that the gradient of a model is a special case of a more general formulation using semirings. This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics.
  arXiv  Detail & Related papers  (2023-07-06T15:19:53Z)
• MAUVE Scores for Generative Models: Theory and Practice [95.86006777961182]
  We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. We find that MAUVE can quantify the gaps between the distributions of human-written text and those of modern neural language models. We demonstrate in the vision domain that MAUVE can identify known properties of generated images on par with or better than existing metrics.
  arXiv  Detail & Related papers  (2022-12-30T07:37:40Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• Hierarchical regularization networks for sparsification based learning on noisy datasets [0.0]
  hierarchy follows from approximation spaces identified at successively finer scales. For promoting model generalization at each scale, we also introduce a novel, projection based penalty operator across multiple dimension. Results show the performance of the approach as a data reduction and modeling strategy on both synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-06-09T18:32:24Z)

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.