Deep convolutional models improve predictions of macaque V1 responses to natural images
Fig 10
Training and evaluation on the different stimulus types.
For both conv3_1 features of VGG-19 (left) and CNN-based models, we trained with all and every individual stimulus type (rows) (see Fig 1) and tested on all and every individual type. The VGG model showed good domain transfer in general. The same was true for the data-driven CNN model, although it performed worse overall when trained on only one set of images due to the smaller training sample. There were no substantial differences in performance across image statistics.