论文标题
从卷积神经网络中提取低维度的心理表征
Extracting low-dimensional psychological representations from convolutional neural networks
论文作者
论文摘要
深度神经网络越来越多地用于认知建模中，用作得出复杂刺激（例如图像）的表示。尽管这些网络的预测能力很高，但尚不清楚它们是否还提供了手头任务的有用解释。卷积神经网络表示形式可预测适当适应后的图像的人类相似性判断。但是，这些高维表示很难解释。在这里，我们提出了一种将这些表示形式减少到低维空间的方法，该空间仍然可以预测相似性判断。我们表明，这些低维度的表现也为人类相似性判断的基础因素提供了深刻的解释。
Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.
