区分图像中多个分类预测是来自不同实体还是同一实体的误判。
Peter Parker or Spiderman? Disambiguating Multiple Class Labels
- 通过联合分析输入归因与分割结果，判断多标签预测是否源于同一实体。
- 在ImageNet验证集上对多个模型测试，能有效识别两类预测关系。
- 提供可验证的反事实证据，适合关注模型可解释性的研究者使用。
在监督分类任务中，深度网络推理时常产生多个预测。对于排名前k的两个预测，可能存在两种情况：一是每个预测由输入中不同的实体驱动；二是单一实体同时驱动两个类别预测。后者意味着模型对同一实体类型做出两次错误猜测，二者不可能同时为真。现有可解释性方法通常逐类分析输入归因，难以区分上述情形。本文提出一种新框架，结合现代分割与输入归因技术，实现有效区分。该框架还提供简单可验证的反事实‘证明’，可在不重新运行方法的情况下对输入进行验证。实验表明，该方法在ImageNet验证集和多个模型上表现良好。
原文摘要 · Abstract (English)
In the supervised classification setting, during inference, deep networks typically make multiple predictions. For a pair of such predictions (that are in the top-k predictions), two distinct possibilities might occur. On the one hand, each of the two predictions might be primarily driven by two distinct sets of entities in the input. On the other hand, it is possible that there is a single entity or set of entities that is driving the prediction for both the classes in question. This latter case, in effect, corresponds to the network making two separate guesses about the identity of a single entity type. Clearly, both the guesses cannot be true, i.e. both the labels cannot be present in the input. Current techniques in interpretability research do not readily disambiguate these two cases, since they typically consider input attributions for one class label at a time. Here, we present a framework and method to do so, leveraging modern segmentation and input attribution techniques. Notably, our framework also provides a simple counterfactual "proof" of each case, which can be verified for the input on the model (i.e. without running the method again). We demonstrate that the method performs well for a number of samples from the ImageNet validation set and on multiple models.
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