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CVPR'21 Online Tutorial on Interpretable Machine Learning in Computer Vision CVPR 2021 4th Tutorial on Interpretable Machine Learning for Computer Vision Panel Session at 14:00 - 15:00 EDT, June 20, 2021 Lectures are pre-recorded and have been posted below. Tutorial Lecturers Bolei Zhou CUHK Ari Morcos Facebook AI Research Wojciech Samek Fraunhofer HHI Cynthia Rudin Duke University Overview Complex machine learning models such as deep convolutional neural networks and recursive neural networks have recently made great progress in a wide range of computer vision applications, such as object/scene recognition, image captioning, visual question answering. But they are often perceived as black-boxes. As the models are going deeper in search of better recognition accuracy, it becomes even harder to understand the predictions given by the models and why. Continuing from the 1st Tutorial on Interpretable Machine Learning for Computer Vision at CVPR’18, the 2nd Tutori...
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