Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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Learning an Adversarial Network for Speech Enhancement Under Extremely Low Signal-to-Noise Ratio Conm mapping problem that converts the noisy speech spectrogram to the clean speech spectrogram. On such basis, we propose a robust speech enhancement approach based on deep adversarial learning for extremely low SNR Condition. The deep adversarial network is trained on a few paired spectrograms of the

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DasNet: Dynamic Adaptive Structure for Accelerating Multi-task Convolutional Neural Networklarge common network, deploying multi-task learning on resource constrained devices is a challenging task. To guarantee overall accuracy with less computation, we introduce DasNet, which (1) automatically searches the adaptive common network sub-structure for each task; (2) fine-tunes corresponding

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Self-Adaptive Network Pruningn many industrial scenarios. In this paper, we propose to reduce such cost for CNNs through a self-adaptive network pruning method (.). Our method introduces a general Saliency-and-Pruning Module (SPM) for each convolutional layer, which learns to predict saliency scores and applies pruning for each

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