# DAVIDSTUTZ

Meet me at CVPR'18: Tuesday, June 19th, I will be presenting our work on weakly-supervised 3D shape completion.
17thAPRIL2017

The presented architecture is shown in Figure 2 and consists of $9$ AlexNets [] with shared weights. The final computed/learned representations are fed into two fully connected layers and then to a softmax layer with $64$ outputs. The $64$ different possibilities correspond to one of $64$ different permutations used for the input tiles.
They demonstrate the usefulness of the learned representations on ImageNet and Pascal VOC 2007. They also present an intuitive visualization. To this end, they compute the $L_1$ norm of feature maps in specific layers and present the top 16 patches (from different) images with largest $L_1$ norm. This illustrates that specific feature maps in specific layer correspond to individual semantic concepts. The visualizations are shown in Figure 3.