Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value

CAMs作为基于Shapley值的解释器

CAMs as Shapley Value-based Explainers

提出CRG框架和ShapleyCAM方法，通过合作博弈与二阶泰勒展开提供理论扎实的CAM解释，并在ImageNet上验证其有效性。

Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset

The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in…

Learning to Estimate Shapley Values with Vision Transformers

Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem. Current explanation approaches rely on attention values or input gradients, but these provide a limited view of a model's dependencies. Shapley values offer a theoretically sound alternative, but their computational cost makes them impractical for large, high-dimensional models. In this work, we aim to make Shapley values practical for vision transformers (ViTs). To do so, we first leverage an attention masking approach to evaluate ViTs with partial…

Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search

In this paper, we propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost. However, the magnitude of architecture parameters updated by gradient descent fails to reveal the actual operation importance to the task performance and therefore harms the effectiveness of obtained architectures. By contrast, we propose to evaluate the direct influence of o…

HarsanyiNet：在单次前向传播中计算精确 Shapley 值

HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation

本文提出 HarsanyiNet 架构，将 Shapley 值重构为 Harsanyi 交互的重新分配，从而在单次前向传播中同时完成推理并精确计算输入变量归因，解决了传统方法计算成本过高的问题。

Neuron Shapley: Discovering the Responsible Neurons

We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns. Interestingly, removing just 30 filters with the highest Shapley scores effectively destroys the prediction accuracy of Inception-v3 on ImageNet. Visualization of these few critical filters provides insights into how the network functions. Neuron Shapley is a flexible framework…