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Advances in generative models increase the need for sample quality assessment.
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Le, H., Samaras, D.: Physics-based shadow image decomposition for shadow removal. IEEE Computer Society, Los Alamitos, CA, USA. https://doi.org/10.1109/TPAMI.2021.3124934
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Le, H., Goncalves, B., Samaras, D., Lynch, H.: Weakly labeling the antarctic: The penguin colony case. In: CVPR Workshops (June 2019)
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Le, H., Samaras, D.: From shadow segmentation to shadow removal. In: European Conference on Computer Vision(ECCV) (2020)
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Xu, J., Le, H., Samaras, D.: Generating features with increased crop-related diversity for few-shot object detection. In: CVPR (2023)
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Durasov, N., Dorndorf, N., Le, H., Fua, P.: Zigzag: Universal sampling-free uncertainty estimation through two-step inference. Transactions on Machine Learning Research (2024)
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Durasov, N., Oner, D., Donier, J., Le, H., Fua, P.: Enabling uncertainty estimation in iterative neural networks. In: Forty-first International Conference on Machine Learning (2024)
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