来源网络：基于样例的端到端可解释性
Provenance Networks: End-to-End Exemplar-Based Explainability
AI总结：
本研究提出来源网络这一新型神经模型，将可解释性嵌入架构，通过关联预测与训练样例实现端到端解释，解决深度学习模型不透明等问题，提升模型透明度与可信度。
摘要：
我们提出了来源网络（provenance networks），这是一类新型神经模型，旨在提供端到端、由训练数据驱动的可解释性。与传统的事后（post-hoc）方法不同，来源网络学习将每个预测直接与其支撑的训练样本相关联，这是模型正常运行的一部分，将可解释性嵌入到架构本身之中。从概念上讲，该模型的运行方式类似于可学习的KNN（K近邻），每个输出都由特征空间中按相关性加权的具体样例提供支撑。\n该方法有助于系统研究记忆与泛化之间的权衡，能够验证给定输入是否包含在训练集中，辅助检测标注错误或异常的数据点，增强对输入扰动的鲁棒性，并支持识别对生成新数据点有贡献的相似输入。通过联合优化主任务和可解释性目标，来源网络能够提供传统深度网络无法给出的模型行为洞察。虽然该模型会引入额外的计算成本，且目前仅适用于中等规模的数据集，但它为现有可解释性技术提供了一种补充方案。特别是，它解决了现代深度学习中的关键挑战，包括模型不透明、幻觉以及数据贡献者的功劳分配问题，从而提升了神经模型的透明度、鲁棒性和可信度。
英文摘要：
We introduce provenance networks, a novel class of neural models designed to provide end-to-end, training-data-driven explainability. Unlike conventional post-hoc methods, provenance networks learn to link each prediction directly to its supporting training examples as part of the model's normal operation, embedding interpretability into the architecture itself. Conceptually, the model operates similarly to a learned KNN, where each output is justified by concrete exemplars weighted by relevance in the feature space. This approach facilitates systematic investigations of the trade-off between memorization and generalization, enables verification of whether a given input was included in the training set, aids in the detection of mislabeled or anomalous data points, enhances resilience to input perturbations, and supports the identification of similar inputs contributing to the generation of a new data point. By jointly optimizing the primary task and the explainability objective, provenance networks offer insights into model behavior that traditional deep networks cannot provide. While the model introduces additional computational cost and currently scales to moderately sized datasets, it provides a complementary approach to existing explainability techniques. In particular, it addresses critical challenges in modern deep learning, including model opaqueness, hallucination, and the assignment of credit to data contributors, thereby improving transparency, robustness, and trustworthiness in neural models.
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