Deep neural network (DNN) models realize human-equivalent performance in tasks such as object recognition. Recent developments in the field have enabled testing the hierarchical similarity of object representation between the human brain and DNNs. However, the representational geometry of object exemplars within a single category using DNNs is unclear. In this study, we investigate which DNN model has the greatest ability to explain invariant within-category object representations by computing the similarity between representational geometries of visual features extracted at the high-level layers of different DNN models. We also test for the invariability of within-category object representations of these models by identifying object exemplars. Our results show that transfer learning models based on ResNet50 best explained both within-category object representation and object identification. These results suggest that the invariability of object representations in deep learning depends not on deepening the neural network but on building a better transfer learning model.

中文翻译：

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哪种深度学习模型可以最好地解释类别内示例的对象表示？

深度神经网络 (DNN) 模型在对象识别等任务中实现了与人类相当的性能。该领域的最新发展已经能够测试人脑和 DNN 之间对象表示的层次相似性。然而，使用 DNN 的单个类别中的对象样本的表示几何尚不清楚。在这项研究中，我们通过计算在不同 DNN 模型的高级层提取的视觉特征的表示几何之间的相似性来研究哪个 DNN 模型最有能力解释不变的类别内对象表示。我们还通过识别对象示例来测试这些模型的类别内对象表示的不变性。我们的结果表明，基于 ResNet50 的迁移学习模型最好地解释了类别内对象表示和对象识别。这些结果表明，深度学习中对象表示的不变性不依赖于加深神经网络，而是依赖于构建更好的迁移学习模型。