EnD：纠缠和解开深度表示以进行偏差校正
EnD: Entangling and Disentangling deep representations for bias correction
摘要：
人工神经网络在越来越多的任务中执行最先进的技术，现在它们被用来解决各种各样的任务。存在一些问题，如训练数据中存在偏差，这对这些模型的泛化能力提出了质疑。在这项工作中，我们提出了EnD，这是一种正则化策略，其目的是防止深度模型学习不必要的偏差。特别是，我们在深层神经网络的某个点插入一个“信息瓶颈”，在那里我们解开了关于偏差的信息，仍然让训练任务的有用信息在模型的其余部分向前传播。EnD的一大优势是我们不需要额外的训练复杂性（如解码器或模型中的额外层），因为它是直接应用于训练模型的正则化器。我们的实验表明，EnD有效地提高了无偏测试集的泛化能力，并且可以有效地应用于真实场景，例如从射线图像中去除COVID-19检测中的隐藏偏差。
英文摘要：
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose EnD, a regularization strategy whose aim is to prevent deep models from learning unwanted biases. In particular, we insert an "information bottleneck" at a certain point of the deep neural network, where we disentangle the information about the bias, still letting the useful information for the training task forward-propagating in the rest of the model. One big advantage of EnD is that we do not require additional training complexity (like decoders or extra layers in the model), since it is a regularizer directly applied on the trained model. Our experiments show that EnD effectively improves the generalization on unbiased test sets, and it can be effectively applied on real-case scenarios, like removing hidden biases in the COVID-19 detection from radiographic images.
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。
