Convolutional neural networks (CNN) are representation learning techniques that achieve state-of-the-art performance on almost every image-related, machine learning task. Applying the representation languages build by these models to tasks beyond the one they were originally trained for is a field of interest known as transfer learning for feature extraction. Through this approach, one can apply the image descriptors learnt by a CNN after processing millions of images to any dataset, without an expensive training phase. Contributions to this field have so far focused on extracting CNN features from layers close to the output (e.g., fully connected layers), particularly because they work better when used out-of-the-box to feed a classifier. Nevertheless, the rest of CNN features is known to encode a wide variety of visual information, which could be potentially exploited on knowledge representation and reasoning tasks. In this paper we analyze the behavior of each feature individually, exploring their intra/inter class activations for all classes of three different datasets. From this study we learn that low and middle level features behave very differently to high level features, the former being more descriptive and the latter being more discriminant. We show how low and middle level features can be used for knowledge representation purposes both by their presence or by their absence. We also study how much noise these features may encode, and propose a thresholding approach to discard most of it. Finally, we discuss the potential implications of these results in the context of knowledge representation using features extracted from a CNN.

该研究旨在探讨深度卷积神经网络中的各个特征的辨别力以及应用它们进行知识表示和推理的可能性，并通过统计学方法提供了新的见解和阈值方法来减少特征中的噪声。

卷积网络特征提取行为研究