A Unsupervised Learning of Compositional Energy Concepts Appendix

In this supplement, we provide additional empirical visualizations of our approach in Section A.1. Next, we provide details on experimental setup in Section A.2. Mean and standard deviation (s.d.) metric scores across 3 random seeds In COMET we utilize a residual network to parameterize an underlying energy function. We illustrate the underlying architecture of the energy function in Figure 2. The energy function takes as input an image at We remove normalization layers from our residual network. To infer global factors from an input image, we utilize a convolutional encoder in Figure 3. We illustrate the overall architecture in Figure 4. Training Details.