%0 Generic %1 hjelm2017boundaryseeking %A Hjelm, R Devon %A Jacob, Athul Paul %A Che, Tong %A Cho, Kyunghyun %A Bengio, Yoshua %D 2017 %K dro %T Boundary-Seeking Generative Adversarial Networks %U http://arxiv.org/abs/1702.08431 %X We introduce a novel approach to training generative adversarial networks, where we train a generator to match a target distribution that converges to the data distribution at the limit of a perfect discriminator. This objective can be interpreted as training a generator to produce samples that lie on the decision boundary of a current discriminator in training at each update, and we call a GAN trained using this algorithm a boundary-seeking GAN (BS-GAN). This approach can be used to train a generator with discrete output when the generator outputs a parametric conditional distribution. We demonstrate the effectiveness of the proposed algorithm with discrete image data. In contrary to the proposed algorithm, we observe that the recently proposed Gumbel-Softmax technique for re-parametrizing the discrete variables does not work for training a GAN with discrete data. Finally, we notice that the proposed boundary-seeking algorithm works even with continuous variables, and demonstrate its effectiveness with two widely used image data sets, SVHN and CelebA.

@misc{hjelm2017boundaryseeking, abstract = {We introduce a novel approach to training generative adversarial networks, where we train a generator to match a target distribution that converges to the data distribution at the limit of a perfect discriminator. This objective can be interpreted as training a generator to produce samples that lie on the decision boundary of a current discriminator in training at each update, and we call a GAN trained using this algorithm a boundary-seeking GAN (BS-GAN). This approach can be used to train a generator with discrete output when the generator outputs a parametric conditional distribution. We demonstrate the effectiveness of the proposed algorithm with discrete image data. In contrary to the proposed algorithm, we observe that the recently proposed Gumbel-Softmax technique for re-parametrizing the discrete variables does not work for training a GAN with discrete data. Finally, we notice that the proposed boundary-seeking algorithm works even with continuous variables, and demonstrate its effectiveness with two widely used image data sets, SVHN and CelebA.}, added-at = {2017-02-28T21:18:45.000+0100}, author = {Hjelm, R Devon and Jacob, Athul Paul and Che, Tong and Cho, Kyunghyun and Bengio, Yoshua}, biburl = {https://www.bibsonomy.org/bibtex/26b3a31a1555455be3f70f1c203f16f76/jonathandinu}, description = {[1702.08431] Boundary-Seeking Generative Adversarial Networks}, interhash = {74ce108eaea93062b20ba3997657e70f}, intrahash = {6b3a31a1555455be3f70f1c203f16f76}, keywords = {dro}, note = {cite arxiv:1702.08431}, timestamp = {2017-02-28T21:18:45.000+0100}, title = {Boundary-Seeking Generative Adversarial Networks}, url = {http://arxiv.org/abs/1702.08431}, year = 2017 }