Implicit networks are a class of neural networks whose outputs are defined by the fixed point of a parameterized operator. They have enjoyed success in many applications including natural language processing, image processing, and numerous other applications. While they have found abundant empirical success, theoretical work on its generalization is still under-explored. In this work, we consider a large family of implicit networks defined parameterized contractive fixed point operators. We show a generalization bound for this class based on a covering number argument for the Rademacher complexity of these architectures.

翻译：隐式网络是一类通过参数化算子的不动点来定义输出的神经网络。它们在自然语言处理、图像处理及众多其他应用中取得了显著成功。尽管在实证层面获得了广泛验证，其泛化能力的理论研究仍相对不足。本文研究由参数化压缩不动点算子定义的一大类隐式网络。基于对该类架构Rademacher复杂度的覆盖数论证，我们给出了此类网络的泛化界限。