Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;
Viterbi School of Engineering, University of Southern California, Los Angeles, CA;
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;
University of Chinese Academy of Sciences, Beijing, China;
Face; Task analysis; Face recognition; Gallium nitride; Hidden Markov models; Decoding; Training;
机译:虚拟培训通过生成对冲网络生成高光谱图像分类
机译:基于改进的深度卷积生成对策网络的虚拟生成路面裂缝图像
机译:从MR图像转移深生成的对抗网络模型
机译:具有Perceptron生成对抗网络的标准化面部图像生成
机译:用于使用实时图像检测的生成对抗网络数据在侧扫声明图像中使用
机译:使用循环一致的生成对冲网络正常化HE-STAIS组织学图像
机译:使用循环一致的生成对冲网络正常化HE-STAIS组织学图像