CNNs,23,24 a variant of multilayer feed forward networks, are recently used widely in image classification and object recognition tasks. A CNN architecture can be designed using a few convolutional layers, often followed by a max pooling layer, then fully connected layers and an activation function layer. As CNN consists of many layers, it needs to learn many connection weights, and for a big network, a lot of data are typically needed to avoid under- or overfitting. The dataset we were using has just 276 cases for training, which is rather small for a CNN. So, a transfer learning approach was tried using a large network trained on the ImagNet set of camera images. 解释

CNNs是一种多层前馈网络的变体，最近在图像分类和物体识别任务中被广泛使用。CNN的架构可以使用几个卷积层，通常跟随一个最大池化层，然后是全连接层和激活函数层。由于CNN由许多层组成，因此需要学习许多连接权重，对于大型网络，通常需要大量数据来避免欠拟合或过拟合。我们使用的数据集只有276个训练案例，对于CNN而言相对较小。因此，尝试使用在ImagNet数据集上训练的大型网络进行迁移学习的方法。