AutoTransfer：生物信号数据删失表示的受试者迁移学习
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
摘要：
我们提供了一个正规化框架的主题迁移学习中，我们试图训练的编码器和分类器，以尽量减少分类损失，受到惩罚的潜在表示和主题标签之间的独立性测量。我们引入了三个概念的独立性和相应的惩罚条款使用互信息或分歧作为代理的独立性。对于每个惩罚项，我们提供了几个具体的估计算法，使用分析方法以及神经批评函数。我们提供了一个放手的策略，将这个多样化的正则化算法家族应用到一个新的数据集，我们称之为“自动转移”。我们评估了这些个体正则化策略和我们的AutoTransfer方法在EEG，EMG和ECoG数据集上的性能，表明这些方法可以改善具有挑战性的真实世界数据集的主题迁移学习。
英文摘要：
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label. We introduce three notions of independence and corresponding penalty terms using mutual information or divergence as a proxy for independence. For each penalty term, we provide several concrete estimation algorithms, using analytic methods as well as neural critic functions. We provide a hands-off strategy for applying this diverse family of regularization algorithms to a new dataset, which we call "AutoTransfer". We evaluate the performance of these individual regularization strategies and our AutoTransfer method on EEG, EMG, and ECoG datasets, showing that these approaches can improve subject transfer learning for challenging real-world datasets.
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。
