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Appendix
Branch-and-bound Our work is Bbased on branch-and-boundBound (BaB), a powerful framework for -2.0 0.5 Lower bound = min(-2.0,
VisualConceptsTokenization Appendix
This is quite similar to what VCT can learn on the synthesized dataset Objects-Room. As the real-world dataset is more diverse, we observe several failure cases shown in Figure 8. We suppose those failure cases are due to VCT, trained withreconstruction loss,isnotgoodatsynthesizing counterfactual samples which arefarfromthe data distribution.
Network-to-NetworkTranslationwith ConditionalInvertibleNeuralNetworks - SupplementaryMaterial Overview
To handle this setting, we use the encoderΘofthe autoencoder trained onAnimalsalso forΦ,butalways apply a spatial deformation to its inputs.