用高能力反事实解释低感知模型能力
Explaining Low Perception Model Competency with High-Competency Counterfactuals
AI总结：
本文提出五种生成高能力反事实图像的方法，用于解释模型预测不确定性的原因，并通过实验验证了其在生成语言解释中的有效性。
摘要：
存在许多方法可以解释图像分类模型如何生成其决策，但非常少有工作探索了解释分类器为何缺乏预测信心的方法。由于分类器失去信心有各种原因，该模型不仅应表明其不确定性水平，还应解释为何不确定。反事实图像已被用于可视化对图像进行更改以生成不同分类决策的变化。在本工作中，我们探索了使用反事实来解释低模型能力——一种预测不确定性的通用形式。为此，我们开发了五种新的方法来生成高能力反事实图像，即图像梯度下降（IGD）、特征梯度下降（FGD）、自动编码器重建（Reco）、潜在梯度下降（LGD）和潜在最近邻（LNN）。我们在两个包含六种已知低模型能力原因的图像数据集中评估了这些方法，发现Reco、LGD和LNN是反事实生成最有力的方法。我们进一步评估了这些三种方法如何被预训练的多模态大语言模型（MLLM）利用，以生成低模型能力的语言解释。我们发现，在语言模型查询中包含反事实图像大大提高了模型生成准确解释的能力，从而证明了反事实图像在解释低感知模型能力方面的效用。
英文摘要：
There exist many methods to explain how an image classification model generates its decision, but very little work has explored methods to explain why a classifier might lack confidence in its prediction. As there are various reasons the classifier might lose confidence, it would be valuable for this model to not only indicate its level of uncertainty but also explain why it is uncertain. Counterfactual images have been used to visualize changes that could be made to an image to generate a different classification decision. In this work, we explore the use of counterfactuals to offer an explanation for low model competency--a generalized form of predictive uncertainty that measures confidence. Toward this end, we develop five novel methods to generate high-competency counterfactual images, namely Image Gradient Descent (IGD), Feature Gradient Descent (FGD), Autoencoder Reconstruction (Reco), Latent Gradient Descent (LGD), and Latent Nearest Neighbors (LNN). We evaluate these methods across two unique datasets containing images with six known causes for low model competency and find Reco, LGD, and LNN to be the most promising methods for counterfactual generation. We further evaluate how these three methods can be utilized by pre-trained Multimodal Large Language Models (MLLMs) to generate language explanations for low model competency. We find that the inclusion of a counterfactual image in the language model query greatly increases the ability of the model to generate an accurate explanation for the cause of low model competency, thus demonstrating the utility of counterfactual images in explaining low perception model competency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
