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A step towards procedural terrain generation with GANs., and . CoRR, (2017)Manifold Mixup: Better Representations by Interpolating Hidden States., , , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 6438-6447. PMLR, (2019)ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events., , , , , and . NIPS, page 3402-3413. (2017)Overcoming challenges in leveraging GANs for few-shot data augmentation., , , , , and . CoLLAs, volume 199 of Proceedings of Machine Learning Research, page 255-280. PMLR, (2022)Exploring validation metrics for offline model-based optimisation with diffusion models., , , and . Trans. Mach. Learn. Res., (2024)Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events, , , , , and . Advances in neural information processing systems, (2017)Unimodal Probability Distributions for Deep Ordinal Classification., and . ICML, volume 70 of Proceedings of Machine Learning Research, page 411-419. PMLR, (2017)Adversarial Mixup Resynthesizers., , , , , , and . DGS@ICLR, OpenReview.net, (2019)A simple squared-error reformulation for ordinal classification., and . CoRR, (2016)On Adversarial Mixup Resynthesis., , , , , , , and . NeurIPS, page 4348-4359. (2019)