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MULTI-TASK LEARNING VIA GRADIENT SPLIT FOR RICH HUMAN ANALYSIS
MULTI-TASK LEARNING VIA GRADIENT SPLIT FOR RICH HUMAN ANALYSIS
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机译:基于梯度分割的多任务学习,用于丰富的人类分析
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摘要
A method for multi-task learning via gradient split for rich human analysis is presented. The method includes extracting images from training data having a plurality of datasets, each dataset associated with one task, feeding the training data into a neural network model including a feature extractor and task-specific heads, wherein the feature extractor has a feature extractor shared component and a feature extractor task-specific component, dividing filters of deeper layers of convolutional layers of the feature extractor into N groups, N being a number of tasks, assigning one task to each group of the N groups, and manipulating gradients so that each task loss updates only one subset of filters.
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