论文标题
近端映射以进行深度正则化
Proximal Mapping for Deep Regularization
论文作者
论文摘要
深度学习成功的基础是有效的正规化，可以使数据中的各种先验进行建模。例如，对对抗性扰动的鲁棒性以及多种方式之间的相关性。但是，大多数正规化器都是根据隐藏层输出来指定的，这些输出本身不是优化变量。与普遍的方法通过模型权重间接优化它们的普遍方法相反，我们建议将近端映射插入到深网的新层，该层直接和明确地产生了正则良好的隐藏层输出。所得的技术与内核翘曲和辍学良好连接，并开发了新颖的算法，用于稳健的时间学习和多视图建模，两者都优于最先进的方法。
Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled. For example, robustness to adversarial perturbations, and correlations between multiple modalities. However, most regularizers are specified in terms of hidden layer outputs, which are not themselves optimization variables. In contrast to prevalent methods that optimize them indirectly through model weights, we propose inserting proximal mapping as a new layer to the deep network, which directly and explicitly produces well regularized hidden layer outputs. The resulting technique is shown well connected to kernel warping and dropout, and novel algorithms were developed for robust temporal learning and multiview modeling, both outperforming state-of-the-art methods.
