%0 Generic %1 mirman2020robustness %A Mirman, Matthew %A Gehr, Timon %A Vechev, Martin %D 2020 %K generative-models iclr2020 robustness %T Robustness Certification of Generative Models %U http://arxiv.org/abs/2004.14756 %X Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation. However, certifying that all images captured by the resulting path in the image manifold satisfy a given property can be very challenging. This is because this set is highly non-convex, thwarting existing scalable robustness analysis methods, which are often based on convex relaxations. We present ApproxLine, a scalable certification method that successfully verifies non-trivial specifications involving generative models and classifiers. ApproxLine can provide both sound deterministic and probabilistic guarantees, by capturing either infinite non-convex sets of neural network activation vectors or distributions over such sets. We show that ApproxLine is practically useful and can verify interesting interpolations in the networks latent space.

@misc{mirman2020robustness, abstract = {Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation. However, certifying that all images captured by the resulting path in the image manifold satisfy a given property can be very challenging. This is because this set is highly non-convex, thwarting existing scalable robustness analysis methods, which are often based on convex relaxations. We present ApproxLine, a scalable certification method that successfully verifies non-trivial specifications involving generative models and classifiers. ApproxLine can provide both sound deterministic and probabilistic guarantees, by capturing either infinite non-convex sets of neural network activation vectors or distributions over such sets. We show that ApproxLine is practically useful and can verify interesting interpolations in the networks latent space.}, added-at = {2020-05-03T04:51:41.000+0200}, author = {Mirman, Matthew and Gehr, Timon and Vechev, Martin}, biburl = {https://www.bibsonomy.org/bibtex/29aeb4cac70c6699e19900568ba0587c5/kirk86}, description = {[2004.14756] Robustness Certification of Generative Models}, interhash = {3e53f1b96b6eab57c5392f4d9ca18d74}, intrahash = {9aeb4cac70c6699e19900568ba0587c5}, keywords = {generative-models iclr2020 robustness}, note = {cite arxiv:2004.14756Comment: Prior version submitted to ICLR 2020}, timestamp = {2020-05-03T04:51:41.000+0200}, title = {Robustness Certification of Generative Models}, url = {http://arxiv.org/abs/2004.14756}, year = 2020 }