We introduce Iterated Integrated Attributions (IIA) - a generic method for
explaining the predictions of vision models. IIA employs iterative integration
across the input image, the internal representations generated by the model,
and their gradients, yielding precise and focused explanation maps. We
demonstrate the effectiveness of IIA through comprehensive evaluations across
various tasks, datasets, and network architectures. Our results showcase that
IIA produces accurate explanation maps, outperforming other state-of-the-art
explanation techniques.

通过迭代整合属性（IIA）方法来解释视觉模型的预测，IIA 方法通过迭代整合输入图像、模型生成的内部表示以及它们的梯度，得到精确而聚焦的解释图。我们通过对各种任务、数据集和网络架构的综合评估验证了 IIA 的有效性，研究结果表明 IIA 产生了准确的解释图，优于其他最先进的解释技术。

迭代集成归因的视觉解释

Visual Explanations via Iterated Integrated Attributions

We present Learning to Explain (LTX), a model-agnostic framework designed for
providing post-hoc explanations for vision models. The LTX framework introduces
an "explainer" model that generates explanation maps, highlighting the crucial
regions that justify the predictions made by the model being explained. To
train the explainer, we employ a two-stage process consisting of initial
pretraining followed by per-instance finetuning. During both stages of
training, we utilize a unique configuration where we compare the explained
model's prediction for a masked input with its original prediction for the
unmasked input. This approach enables the use of a novel counterfactual
objective, which aims to anticipate the model's output using masked versions of
the input image. Importantly, the LTX framework is not restricted to a specific
model architecture and can provide explanations for both Transformer-based and
convolutional models. Through our evaluations, we demonstrate that LTX
significantly outperforms the current state-of-the-art in explainability across
various metrics.

通过 Learning to Explain（LTX）模型 - 无关框架，我们提供视觉模型后期解释的高亮关键区域的解释图，实现对基于 Transformer 和卷积模型的解释性，显著优于当前各种度量的最新工作。

学习解释：一种对黑盒模型解释的模型无关框架

Learning to Explain: A Model-Agnostic Framework for Explaining Black Box  Models

This paper presents Deep Integrated Explanations (DIX) - a universal method
for explaining vision models. DIX generates explanation maps by integrating
information from the intermediate representations of the model, coupled with
their corresponding gradients. Through an extensive array of both objective and
subjective evaluations spanning diverse tasks, datasets, and model
configurations, we showcase the efficacy of DIX in generating faithful and
accurate explanation maps, while surpassing current state-of-the-art methods.

该研究提出了 Deep Integrated Explanations（DIX）—— 一种用于解释视觉模型的通用方法，通过整合模型的中间表示和相应的梯度来生成解释图，通过广泛的客观和主观评估展示了 DIX 在生成准确和可靠的解释图方面超越了当前的最先进方法。