This study presents a new approach of using multitask learning deep neural network (MTLDNN) to combine RP and SP data and incorporate the traditional nest logit approach as a special case. Based on a combined RP and SP survey in Singapore to examine the demand for autonomous vehicles (AV),...
1. 论文翻译:2020_RESIDUAL ACOUSTIC ECHO SUPPRESSION BASED ON EFFICIENT MULTI-TASK CONVOLUTIONAL NEURAL NETWORK(1) 2. 论文翻译:2021_Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation(1) 3. 论文翻译:2020_Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and...
SEANet_torch -> Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images arborizer -> Tree crowns segmentation and classification ReUse -> REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation unet-sentinel -> UNet to handl...
Multi-Task Deep Neural Networks for Natural Language Understanding This PyTorch package implements the Multi-Task Deep Neural Networks (MT-DNN) for Natural Language Understanding, as described in: Xiaodong Liu*, Pengcheng He*, Weizhu Chen and Jianfeng Gao Multi-Task Deep Neural Networks for Natural...
跟RNN的竞争过程中,CNN不停地优化自身能力,现在CNN的研究趋势是:加入GLU/GTU门机制来简化梯度传播,使用Dilated CNN增加覆盖长度,基于一维卷积层叠加深度并用Residual Connections辅助优化,优秀代表:增加网络深度的VDCNN和引进Gate机制的GCNN。 2.2.1 TextCNN
Kim, “Deep Pyramidal Residual Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, vol. 2017-Janua, pp. 6307–6315.[36] A. Khan, A. Sohail, and A. Ali, “A New Channel Boosted Convolutional Neural Network using Transfer Learning,” Apr. 2018.[...
Deep Multi-task Cascaded Acoustic Echo Cancellation and Noise Suppression(论文翻译) 哎哟 电子信息硕士在校生。4 人赞同了该文章 目录 收起 摘要: 1.介绍 2.方法 2.1.问题公式化 2.2.DMC-AEC 3.实验 3.1.数据集和数据准备 3.2.训练详情 3.3.客观评价指标 4.结果 4.1 基线系统 4.2双人对话场景比较 ...
I.PHI may be the first multitask dataset of machine-actionable epigraphical text, but its size is still several orders of magnitude smaller than modern typical language datasets. To avert the risk of overfitting, which is common in large-scale deep neural network architectures, we apply several...
[9] Carbajal, G., Serizel, R., Vincent, E., & Humbert, E. (2018, April). Multiple-input neural network-based residual echo suppression. In ICASSP 2018-IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1-5). ...
Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection, CVPR 2022, Jinyuan Liu et al.[PDF][Code] DetFusion: A Detection-driven Infrared and Visible Image Fusion Network, ACM Multimedia 2022, Yiming Sun et al. [PDF...