XtrAIn 解决了传统遮挡归因方法中基线选择和归因偏移的痛点，做可解释 AI 研究的开发者可以直接用，生成更干净的归因图，值得关注。
XtrAIn 是一种新的特征归因方法，通过将遮挡操作从输入空间转移到参数空间，避免了传统遮挡方法中基线选择带来的偏差和分布外样本问题。它沿着模型训练轨迹，测量特征相关参数更新对输出 logits 的影响，从而生成更稳定、更可解释的归因图。实验在控制图像数据集和 PAM50 乳腺癌亚型分类上验证了其有效性。XtrAIn 还提供了轻量级近似 Xstep 和目标聚焦变体 XtrAIn+，以降低计算成本并增强类别特异性。该方法为理解模型训练过程中特征证据的形成提供了新视角。
XtrAIn: Training-Guided Occlusion for Feature Attribution
Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output. However, their reliability is strongly affected by how feature removal is implemented: externally selected baselines can introduce bias, out-of-distribution samples, and unstable explanations, while in nonlinear models the occlusion of a set of features can also alter the contribution of non-occluded features. We refer to this effect as attribution shift, as the attribution scores of the non-occluded features drift from their initial values. To challenge these major issues that render explanations unstable, we introduce XtrAIn, a training-guided attribution method that transfers the occlusion operation from the input space to the parameter space. Instead of replacing input values with hand-crafted baselines, XtrAIn follows the model's training trajectory and measures how feature-associated parameter updates affect the output logits. We further introduce Xstep, a lightweight approximation for reducing computational cost, and XtrAIn+, a target-focused variant that emphasizes updates aligned with the target class. Experiments on controlled image datasets and PAM50 breast-cancer subtype classification show that the proposed methods produce cleaner and more interpretable attribution patterns than standard attribution baselines. Overall, XtrAIn provides a training-aware perspective on feature attribution and offers a useful diagnostic tool for studying how feature-level evidence is formed during training.