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Behind the leaves - Estimation of occluded grapevine berries with conditional generative adversarial networks.

, , , , , and . CoRR, (2021)

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Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower., and . CoRR, (2023)Controlled Multi-modal Image Generation for Plant Growth Modeling., , and . ICPR, page 5118-5124. IEEE, (2022)Occlusion Sensitivity Analysis of Neural Network Architectures for Eddy Detection., , , and . IGARSS, page 623-626. IEEE, (2022)A survey of uncertainty in deep neural networks., , , , , , , , , and 4 other author(s). Artif. Intell. Rev., 56 (S1): 1513-1589 (October 2023)Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks., , , and . GCPR, volume 13485 of Lecture Notes in Computer Science, page 479-494. Springer, (2022)Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery., , and . IEEE Geosci. Remote. Sens. Lett., (2024)Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery., , and . CoRR, (2023)Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks., , , , , , , , and . ECCV Workshops (6), volume 12540 of Lecture Notes in Computer Science, page 347-359. Springer, (2020)Detection of Anomalous Grapevine Berries Using All-Convolutional Autoencoders., , and . IGARSS, page 3701-3704. IEEE, (2019)Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer., , , and . GCPR, volume 13485 of Lecture Notes in Computer Science, page 495-510. Springer, (2022)