PixelRNN 956 Real World ICML2016最佳论文，出自于DeepMind 电子科技大学研究生《机器学习》
电子科技大学研究生《机器学习》 PixelRNN Real World ICML2016最佳论文，出自于DeepMind
Wavenet beyond Image 56 Output Hidden Layer Hidden Layer Hidden Layer Audio:Aaron van den Oord,Sander Dieleman,Heiga Zen,Karen Simonyan,Oriol Vinyals,Alex Graves,Nal Kalchbrenner,Andrew Senior,Koray Kavukcuoglu,WaveNet:A Generative Model for Raw Audio,arXiv preprint,2016 Video:Nal Kalchbrenner,Aaron van den Oord,Karen Simonyan,Ivo Danihelka,Oriol Vinyals,Alex Graves,Koray Kavukcuoglu,Video Pixel Networks,arXiv preprint,2016 电子科技大学研究生《机器学习》
电子科技大学研究生《机器学习》 Wavenet – beyond Image Audio: Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu, WaveNet: A Generative Model for Raw Audio, arXiv preprint, 2016 Video: Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu, Video Pixel Networks , arXiv preprint, 2016
例 Any other ideas? 电子科技大学研究生《模式识别》
电子科技大学研究生《模式识别》 Any other ideas?
PixelCNN Still generate image pixels starting from Softmax loss at each pixel corner Dependency on previous pixels now modeled using a CNN over context region Training:maximize likelihood of training images n p(x)=p(xc1,,ci-1） i=1 电子科技大学研究生《机器学习》
电子科技大学研究生《机器学习》 PixelCNN
PixelRNN and PixelCNN Pros: Improving PixelCNN performance Can explicitly compute likelihood - Gated convolutional layers p(x) Short-cut connections Explicit likelihood of training Discretized logistic loss data gives good evaluation Multi-scale metric Training tricks Good samples Etc.… Con: See Sequential generation =slow Van der Oord et al.NIPS 2016 Salimans et al.2017 (PixelCNN++) log.csdn.net/poulang5786 电子科技大学研究生《机器学习》
电子科技大学研究生《机器学习》