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Various methods of measuring unit selectivity have been developed with theaim of better understanding how neural networks work. But the differentmeasures provide divergent estimates of selectivity, and this has led todifferent conclusions regarding the conditions in which selective objectrepresentations are learned and the functional relevance of theserepresentations. In an attempt to better characterize object selectivity, weundertake a comparison of various selectivity measures on a large set of unitsin AlexNet, including localist selectivity, precision, class-conditional meanactivity selectivity (CCMAS), network dissection,the human interpretation ofactivation maximization (AM) images, and standard signal-detection measures. Wefind that the different measures provide different estimates of objectselectivity, with precision and CCMAS measures providing misleadingly highestimates. Indeed, the most selective units had a poor hit-rate or a highfalse-alarm rate (or both) in object classification, making them poor objectdetectors. We fail to find any units that are even remotely as selective as the'grandmother cell' units reported in recurrent neural networks. In order togeneralize these results, we compared selectivity measures on units in VGG-16and GoogLeNet trained on the ImageNet or Places-365 datasets that have beendescribed as 'object detectors'. Again, we find poor hit-rates and highfalse-alarm rates for object classification. We conclude that signal-detectionmeasures provide a better assessment of single-unit selectivity compared tocommon alternative approaches, and that deep convolutional networks of imageclassification do not learn object detectors in their hidden layers.

Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?