基于向量值无限任务学习的情感迁移
Emotion Transfer Using Vector-Valued Infinite Task Learning
摘要：
风格迁移是机器学习的一个重要问题，并有许多成功的应用。在这项工作中，我们提出了一种新的风格转移框架，建立在无限任务学习和向量值再生核希尔伯特空间。我们在情感转移中实例化了这个想法，其目标是将面部图像转换为不同的目标情感。所提出的方法提供了一种原则性的方法来获得显式控制连续的样式空间。我们证明了流行的面部情感基准的技术的效率，实现低重建成本和高的情感分类精度。
英文摘要：
Style transfer is a significant problem of machine learning with numerous successful applications. In this work, we present a novel style transfer framework building upon infinite task learning and vector-valued reproducing kernel Hilbert spaces. We instantiate the idea in emotion transfer where the goal is to transform facial images to different target emotions. The proposed approach provides a principled way to gain explicit control over the continuous style space. We demonstrate the efficiency of the technique on popular facial emotion benchmarks, achieving low reconstruction cost and high emotion classification accuracy.
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