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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Co-Compressing and Unifying Deep CNN Models for Efficient Human Face and Speaker Recognition
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Co-Compressing and Unifying Deep CNN Models for Efficient Human Face and Speaker Recognition

机译：用于高效人体脸部和扬声器识别的共压缩和统一深层CNN模型

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摘要

Deep CNN models have become state-of-the-art techniques in many application, e.g., face recognition, speaker recognition, and image classification. Although many studies address on speedup or compression of individual models, very few studies focus on co-compressing and unifying models from different modalities. In this work, to joint and compress face and speaker recognition models, a shared-codebook approach is adopted to reduce the redundancy of the combined model. Despite the modality of the inputs of these two CNN models are quite different, the shared codebook can support two CNN models of sound and image for speaker and face recognition. Experiments show the promising results of unified and co-compressing heterogeneous models for efficient inference.
机译：深度CNN模型已成为许多应用中的最先进的技术，例如，人脸识别，扬声器识别和图像分类。虽然许多研究关于各个模型的加速或压缩的研究，但很少有研究专注于来自不同方式的共同压缩和统一模型。在这项工作中，为了联合和压缩面部和扬声器识别模型，采用共享 - 码本方法来减少组合模型的冗余。尽管这两个CNN模型的输入的模型非常不同，但共享码本可以支持两个CNN的声音和图像的声音模型，用于扬声器和面部识别。实验表明，统一和共压制异构模型的有希望的结果，以实现有效推理。

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