Computer vision systems and methods for end-to-end training of convolutional neural networks using differentiable dual-decomposition techniques
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United States of America Patent
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Oct 1, 2024
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Jun 17, 2021
app pub date -
Dec 14, 2020
filing date -
Dec 13, 2019
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Abstract
Computer vision systems and methods for end-to end training of neural networks are provided. The system generates a fixed point algorithm for dual-decomposition of a maximum-a-posteriori inference problem and trains the convolutional neural network and a conditional random field with the fixed point algorithm and a plurality of images of a dataset to learn to perform semantic image segmentation. The system can segment an attribute of an image of the dataset by the trained neural network and the conditional random field.
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