用核心数据集缩小模型解释的计算开销，提升效率与稳定性。
Deep Model Interpretation with Limited Data : A Coreset-based Approach
- 通过核心数据集采样技术，选取代表性子集替代全量数据。
- 实验表明该方法在多种模型与解释方法下均保持稳定效果。
- 适合资源有限但需高效可靠解释的场景，如边缘计算。
模型解释旨在从训练好的模型内部提取洞见。常见方法是识别模型中关键的内部特征。尽管近年已有进展，这些方法仍因需密集评估大规模数据集而计算成本高昂。为此，我们提出一种基于核心数据集（coreset）的解释框架，利用核心数据集选择方法从大样本中抽取代表性子集用于解释任务。为此，我们设计了一种基于相似性的评估协议，用于衡量模型解释方法对输入数据量变化的鲁棒性。在多个解释方法、深度神经网络模型及核心数据集选择策略下的实验验证了该框架的有效性。
原文摘要 · Abstract (English)
Model Interpretation aims at the extraction of insights from the internals of a trained model. A common approach to address this task is the characterization of relevant features internally encoded in the model that are critical for its proper operation. Despite recent progress of these methods, they come with the weakness of being computationally expensive due to the dense evaluation of datasets that they require. As a consequence, research on the design of these methods have focused on smaller data subsets which may led to reduced insights. To address these computational costs, we propose a coreset-based interpretation framework that utilizes coreset selection methods to sample a representative subset of the large dataset for the interpretation task. Towards this goal, we propose a similarity-based evaluation protocol to assess the robustness of model interpretation methods towards the amount data they take as input. Experiments considering several interpretation methods, DNN models, and coreset selection methods show the effectiveness of the proposed framework.
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