Fig. 3. Detection of face images using the response of face units in untrained networks.

a Design of the face detection task and SVM classifier using the responses of the untrained AlexNet. During this task, face or non-face images were randomly presented to the networks and the observed response of the final layer was used to train a support vector machine (SVM) to classify whether the given image was a face or not. Among 60 images from each class (face, hand, horn, flower, chair, and scrambled face) that were not used for face unit selection, 40 images were randomly sampled for the training of the SVM, and the other 20 images were used for testing. The images shown are selected examples from the publicly available dataset⁵⁹. The original images are available at [http://vpnl.stanford.edu/fLoc/]. b Performance on the face detection task using a single unit randomly sampled from face-selective units (n = 465) and units without selective responses to any image classes (n = 7776). The chance level was measured by the shuffled responses of face-selective units in the untrained network. The error bar indicates the standard deviation of each unit. Each bar indicates the mean and the error bar indicates the standard deviation of performance of each unit. c Performance of the face detection task using face-selective units and non-selective units when varying the number of units from 1 to 456. The dashed line indicates the detection performance using all units in Conv5 (n = 43,264). Each line indicates the mean and the shaded area indicates the standard deviation for 100 repeated trials of the random sampling of units. d Performance on the face detection task using face-selective units (n = 465) and then using all units in Conv5 (n = 43,264). Each bar indicates the mean and the error bar indicates the standard deviation for 100 repeated trials of the random sampling of units.