并非每个受试者都应保留：面向噪声投入度识别的机器遗忘
Not Every Subject Should Stay: Machine Unlearning for Noisy Engagement Recognition
AI总结：
本研究提出受试者层面的机器遗忘作为投入度识别的事后净化机制，通过模型相关代理排序有害受试者并应用近似遗忘更新，在DAiSEE和EngageNet上以约四分之一重训练成本恢复神谕模型89.3%和92.5%的增益，验证了其作为低成本修正机制的有效性。
摘要：
投入度识别数据集通常按受试者索引，且往往包含噪声、主观的监督信号，这使得事后数据集修订成为一个实际问题。现有的噪声标签和数据清洗方法大多在训练前或训练期间于样本层面运作，但并未直接解决一个不同的问题：一旦模型已经训练完成，能否在不进行完整重训练的情况下，移除整个问题受试者的影响？我们通过受试者层面的机器遗忘作为投入度识别的事后净化机制来研究这一设定。从在所有受试者上训练的基线模型出发，我们使用模型相关的代理指标对候选有害受试者进行排序，应用轻量级的近似遗忘更新，并将结果与仅在保留受试者上从头重训练的神谕模型进行比较。我们在DAiSEE和EngageNet上使用张量卷积和卷积变换网络（TCCT-Net）作为固定平台实例化该协议，并在相同的移除场景下评估三个匹配的模型状态：基线、遗忘后和神谕。在代表性的K=3遗忘集设置中，遗忘模型在EngageNet和DAiSEE上分别恢复了神谕增益的89.3%和92.5%，而重训练成本约为其四分之一。在测试的小规模审计机制中，效果在中等遗忘集规模时最强，表明近似受试者层面的遗忘是一种有用的低成本修正机制，但其收益取决于受试者选择质量和移除机制。
英文摘要：
Engagement recognition datasets are typically subject-indexed and often contain noisy, subjective supervision, making post-hoc dataset revision a practical problem. Existing noisy-label and data-cleaning methods largely operate at the sample level before or during training, but do not directly address a different question: once a model has already been trained, can the influence of an entire problematic subject be removed without full retraining? We study this setting through subject-level machine unlearning as a post-hoc sanitization mechanism for engagement recognition. Starting from a baseline trained on all subjects, we rank candidate harmful subjects using a model-dependent proxy, apply a lightweight approximate unlearning update, and compare the result against an oracle model retrained from scratch on the retained subjects only. We instantiate this protocol on DAiSEE and EngageNet using Tensor-Convolution and Convolution-Transformer Network (TCCT-Net) as a fixed platform and evaluate three matched model states under the same removal scenario: baseline, unlearned, and oracle. In representative K=3 forget-set settings, the unlearned model recovers 89.3% and 92.5% of the oracle gain on EngageNet and DAiSEE, respectively, at roughly one quarter of retraining cost. Across the tested small-audit regimes, effectiveness is strongest at an intermediate forget-set size, indicating that approximate subject-level unlearning is a useful low-cost correction mechanism, but one whose benefit depends on subject selection quality and removal regime.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
