Learning powerful feature representations with CNNs is hard when training
data are limited. Pre-training is one way to overcome this, but it requires
large datasets sufficiently similar to the target domain. Another option is to
design priors into the model, which can range from tuned hyperparameters to
fully engineered representations like Scattering Networks. We combine these
ideas into structured receptive field networks, a model which has a fixed
filter basis and yet retains the flexibility of CNNs. This flexibility is
achieved by expressing receptive fields in CNNs as a weighted sum over a fixed
basis which is similar in spirit to Scattering Networks. The key difference is
that we learn arbitrary effective filter sets from the basis rather than
modeling the filters. This approach explicitly connects classical multiscale
image analysis with general CNNs. With structured receptive field networks, we
improve considerably over unstructured CNNs for small and medium dataset
scenarios as well as over Scattering for large datasets. We validate our
findings on ILSVRC2012, Cifar-10, Cifar-100 and MNIST. As a realistic small
dataset example, we show state-of-the-art classification results on popular 3D
MRI brain-disease datasets where pre-training is difficult due to a lack of
large public datasets in a similar domain.

通过将 “结构化感受野网络” 与可微的感受野表示方法相结合，我们既充分利用了预训练的优势，又避免了在小数据集上使用卷积神经网络面临过拟合的问题，该方法在多个数据集上进行了验证并得到了显著的提高。

卷积神经网络中的结构化接受域

Structured Receptive Fields in CNNs

Despite the effectiveness of Convolutional Neural Networks (CNNs) for image
classification, our understanding of the relationship between shape of
convolution kernels and learned representations is limited. In this work, we
explore and employ the relationship between shape of kernels which define
Receptive Fields (RFs) in CNNs for learning of feature representations and
image classification. For this purpose, we first propose a feature
visualization method for visualization of pixel-wise classification score maps
of learned features. Motivated by our experimental results, and observations
reported in the literature for modeling of visual systems, we propose a novel
design of shape of kernels for learning of representations in CNNs. In the
experimental results, we achieved a state-of-the-art classification performance
compared to a base CNN model [28] by reducing the number of parameters and
computational time of the model using the ILSVRC-2012 dataset [24]. The
proposed models also outperform the state-of-the-art models employed on the
CIFAR-10/100 datasets [12] for image classification. Additionally, we analyzed
the robustness of the proposed method to occlusion for classification of
partially occluded images compared with the state-of-the-art methods. Our
results indicate the effectiveness of the proposed approach. The code is
available in github.com/minogame/caffe-qhconv.

通过对卷积核的形状与特征表示学习之间的关系的探究，结合实验结果和模拟视觉系统的观察，提出了一种新的卷积核形状设计方法，该方法在 ILSVRC-2012 数据集上减少了模型参数和计算时间，在 CIFAR-10/100 数据集上也优于现有的模型，进一步分析了该方法对于遮挡图像的分类的鲁棒性，并展示了显著的分类性能。