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High-Fidelity Image Generation With Fewer Labels.

M. Lucic, M. Tschannen, M. Ritter, X. Zhai, O. Bachem, and S. Gelly. ICML, volume 97 of Proceedings of Machine Learning Research, page 4183-4192. PMLR, (2019)

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Mario Fuksa University of Stuttgart

Mario Heene

Mario Döbler University of Stuttgart

Mario Gaimann University of Stuttgart

Mario Hermle University of Stuttgart

Other publications of authors with the same name

A Large-Scale Study on Regularization and Normalization in GANs.K. Kurach, M. Lucic, X. Zhai, M. Michalski, and S. Gelly. ICML, volume 97 of Proceedings of Machine Learning Research, page 3581-3590. PMLR, (2019)Recent Advances in Autoencoder-Based Representation Learning.M. Tschannen, O. Bachem, and M. Lucic. CoRR, (2018)Self-Supervised Learning of Video-Induced Visual Invariances.M. Tschannen, J. Djolonga, M. Ritter, A. Mahendran, N. Houlsby, S. Gelly, and M. Lucic. CoRR, (2019)Self-Supervised GANs via Auxiliary Rotation Loss.T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby. CVPR, page 12154-12163. Computer Vision Foundation / IEEE, (2019)Scalable and Distributed Clustering via Lightweight Coresets.O. Bachem, M. Lucic, and A. Krause. CoRR, (2017)Are GANs Created Equal? A Large-Scale Study.M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet. CoRR, (2017)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.F. Locatello, S. Bauer, M. Lucic, S. Gelly, B. Schölkopf, and O. Bachem. CoRR, (2018)One-Shot Coresets: The Case of k-Clustering.O. Bachem, M. Lucic, and S. Lattanzi. CoRR, (2017)Uniform Deviation Bounds for k-Means Clustering.O. Bachem, M. Lucic, S. Hassani, and A. Krause. ICML, volume 70 of Proceedings of Machine Learning Research, page 283-291. PMLR, (2017)Distributed and Provably Good Seedings for k-Means in Constant Rounds.O. Bachem, M. Lucic, and A. Krause. ICML, volume 70 of Proceedings of Machine Learning Research, page 292-300. PMLR, (2017)