Paper accepted at ICLR 2018
We are very pleased to announce that our group in collaboration with Fraunhofer IAIS got a paper accepted for poster presentation at ICLR 2018 : The Sixth International Conference on Learning Representations, which will be held on April 30 - May 03, 2018 in Vancouver Convention Center, Vancouver CANADA.The Sixth edition of ICLR will offer many opportunities to present and discuss latest advances in the performance of machine learning methods and deep learning. With a broad view of the field and include topics such as feature learning, metric learning, compositional modeling, structured prediction, reinforcement learning, and issues regarding large-scale learning and non-convex optimization. The range of domains to which these techniques apply is also very broad, from vision to speech recognition, text understanding, gaming, music, etc. Here is the accepted paper with its abstract:
- “On the regularization of Wasserstein GANs” by Henning Petzka, Asja Fischer, Denis Lukovnikov
Abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. Augmenting the loss by a regularization term that penalizes the deviation of the gradient norm of the critic (as a function of the network's input) from one, was proposed as an alternative that improves training. We present theoretical arguments why using a weaker regularization term enforcing the Lipschitz constraint is preferable. These arguments are supported by experimental results on several data sets.
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