Fig. 3.

The emergence of object- and part-specific units within a Progressive GAN generator (19). (A) The analyzed Progressive GAN consists of 15 convolutional layers that transform a random input vector into a synthesized image of a kitchen. (B) A single filter is visualized as the region of the output image where the filter activates beyond its top 1% quantile level; note that the filters are all precursors to the output. (C) Dissecting all of the layers of the network shows a peak in object-specific units at layer 5 of the network. (D) A detailed examination of layer 5 shows more part-specific units than objects and many visual concepts corresponding to multiple units. (E) Units do not correspond to exact pixel patterns: A wide range of visual appearances for ovens and chairs is generated when an oven or chair part unit is activated. (F) When a unit specific to window parts is tested as a classifier, on average the unit activates more strongly on generated images that contain large windows than images that do not. The jitter plot shows the peak activation of unit 314 on 800 generated images that have windows larger than 5% of the image area as estimated by a segmentation algorithm and 800 generated images that do not. (G) Some counterexamples: images for which unit 314 does not activate but where windows are synthesized nevertheless.