Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the “fixed filters” principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never learned, and the “nimbleness” principle that only few network parameters suffice. We contribute (a) visual model-based explanations, (b) speed and accuracy gains, and (c) novel tools for deep convolutional neural networks. ExplainFix gives key insights that spatially fixed networks should have a steered initialization, that spatial convolution layers tend to prioritize low frequencies, and that most network parameters are not necessary in spatially fixed models. ExplainFix models have up to ×100 fewer spatial filter kernels than fully learned models and matching or improved accuracy. Our extensive empirical analysis confirms that ExplainFix guarantees nimbler models (train up to 17% faster with channel pruning), matching or improved predictive performance (spanning 13 distinct baseline models, four architectures and two medical image datasets), improved robustness to larger learning rate, and robustness to varying model size. We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.

中文翻译：

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ExplainFix：可解释的空间固定深度网络

是否有不需要学习的深度网络初始化？ExplainFix采用了两个设计原则：卷积神经网络的所有空间滤波器权重可以在初始化时固定且永远不会学习的“固定过滤器”原则，以及只需很少的网络参数就足够的“灵活”原则。我们贡献了 (a)基于视觉模型的解释，(b)速度和准确性的提升，以及 (c)用于深度卷积神经网络的新工具。解释修复给出了关键的见解，即空间固定网络应具有受控初始化，空间卷积层倾向于优先考虑低频，并且大多数网络参数在空间固定模型中不是必需的。ExplainFix模型的空间滤波器内核比完全学习的模型少100倍，并且匹配或提高了准确性。我们广泛的实证分析证实，ExplainFix保证更灵活的模型（通过通道修剪将训练速度提高17 %）、匹配或改进预测性能（跨越 13 个不同的基线模型、四个架构和两个医学图像数据集）、提高对更大学习率的鲁棒性、以及对不同模型大小的鲁棒性。我们首先要证明最先进的卷积深度网络中的所有空间过滤器都可以在初始化时固定，而不是学习。