论文标题
卷积神经网络的接受田的改进可靠地提高预测性能
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance
论文作者
论文摘要
神经体系结构的最小变化（例如，在关键层中更改单个超参数），可以导致卷积神经网络（CNN）的预测性能显着提高。在这项工作中，我们提出了一种新的接受现场分析的方法，可以在我们的实验中检查的二十种知名的CNN体系结构中产生这些类型的理论和经验性能提高。通过进一步开发和形式化卷积神经网络中接受场扩张的分析，我们可以在训练模型之前以自动化的方式预测非生产性层。这使我们能够以低成本优化给定体系结构的参数效率。我们的方法在计算上很简单，可以自动化，甚至可以用最小的努力来完成大多数常见的体系结构。我们通过提高过去和当前表现最佳的CNN-Architectures的参数效率来证明这种方法的有效性。具体而言，我们的方法能够改善各种知名的，最先进的（SOTA）模型类别的ImagEnet1k性能，包括：VGG Nets，MobilenetV1，Mobilenetv3，Nasnet A（移动），MNASNET，MNASNET，EFFIFENET和CORVNEXT，并为每个模型类别提供了一个新的SOTA。
Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive performance in Convolutional Neural Networks (CNNs). In this work, we present a new approach to receptive field analysis that can yield these types of theoretical and empirical performance gains across twenty well-known CNN architectures examined in our experiments. By further developing and formalizing the analysis of receptive field expansion in convolutional neural networks, we can predict unproductive layers in an automated manner before ever training a model. This allows us to optimize the parameter-efficiency of a given architecture at low cost. Our method is computationally simple and can be done in an automated manner or even manually with minimal effort for most common architectures. We demonstrate the effectiveness of this approach by increasing parameter efficiency across past and current top-performing CNN-architectures. Specifically, our approach is able to improve ImageNet1K performance across a wide range of well-known, state-of-the-art (SOTA) model classes, including: VGG Nets, MobileNetV1, MobileNetV3, NASNet A (mobile), MnasNet, EfficientNet, and ConvNeXt - leading to a new SOTA result for each model class.
