Interpretable Machine Learning for Computer Vision
Recent progress we made on visualization, interpretation, and explanation methodologies for analyzing both the data and the models in computer vision.
computer-vision interpretability cvpr-2020 article video

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.

Previous Interpretable Machine Learning Tutorials

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Authors community post
I want to understand things clearly and explain them well. @openai formerly @brain-research.
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