In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving generative models. In addition, conditioning the generator network on the measurements enables us to achieve much more detailed results. We empirically demonstrate that these advantages provide meaningful solutions to the Fourier and the compressive phase retrieval problem and that our method outperforms well-established projection-based methods as well as existing methods that are based on neural networks. Like other deep learning methods, our approach is very robust to noise and can therefore be very useful for real-world applications.
We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019-Task 6). Our results show that context embeddings are important features for the three different subtasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro F 1 of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.
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