Abstract: It is impractical to have a large size of training dataset in real work for neural network training, so a two-input stream generative neural network which can generate a new image with the given parameters was proposed, hence to augment the training dataset. The framework of the proposed neural network consists of a two-input steam convolution network and a deconvolution network. The two-input steam network has two convolution networks to extract features, and the deconvolution network is connected to the end. Two images with different angle were input into the convolution network to get high-level description, then an interpolation target image with a new perspectives was generated by using the deconvolution network with the above high-level description and set parameters. The experiment results on ShapeNetCore show that on the same dataset, the recognition rate of the proposed network has increased by 20% than the common network framework. This method can enlarge the size of the training dataset and is useful for multi-angle recognition.

Key words: deep learning, artificial intelligence, generative neural network, deconvolution, two-input stream

摘要： 在实际工作中深度学习方法通常不具备大量的训练样本，因此提出了双输入流深度反卷积生成神经网络的构架，依据给定的条件产生新的目标图像，从而扩充训练样本集。该神经网络的整体架构由双输入的卷积网络和一个反卷积网络输出构成，其中双输入卷积网络接收目标物体不同视角的两张图片并提取抽象特征，而反卷积网络则利用抽象特征和设定的参数产生新的插值目标图像。在ShapeNetCore数据集上的实验结果显示，在相同数量的训练样本空间中，与未扩展数据集的卷积网络相比，双输入流深度反卷积生成神经网络的识别率提高了20%左右。结果表明，双输入流深度反卷积生成神经网络无需输入目标物类别，可生成新参数条件下的目标图像，扩充训练样本空间，从而提高识别率，可用于少样本的目标物多角度识别。

关键词: 深度学习, 人工智能, 生成神经网络, 反卷积, 双输入流