dcgan

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an

问题 I have been trying to implement the DCGan, the face book's paper, and blocked by below two issues almost for 2 weeks. Any suggestions would be appreciated. Thanks. Issue 1: DCGAN paper suggest to use BN(Batch Normalization) both the generator and discriminator. But, I couldn't get better result with BN rather than w/out BN. I copied DCGAN model I used which is exactly the same with a DCGAN paper. I don't think it is due to overfitting. Because (1) It keeps showing the noise the same with an

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 8 months ago . If i have a dataset consisting by a list of images each associated with a series of features; there is a model that, once trained, generates new images upon entering a new list of features? 回答1: I think you are looking for GAN(Generative Adversarial Networks) which is proposed in

问题 I am analysing this DCGAN. When I use input_data from tensorflow.examples.tutorials.mnist , as seen in line 144: self.x_train = input_data.read_data_sets("mnist",\ one_hot=True).train.images I obtain reasonably good results: Though when I use mnist from keras.datasets and the 144th line looks like this: (xtr, ytr), (xte, yte) = mnist.load_data(); self.x_train = xtr I get horribly bad results: I have checked manually a few images from both datasets and they are quite similar. So what is the

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . I am trying to use this version of the DCGAN code (implemented in Tensorflow) with some of my data. I run into the problem of the discriminator becoming too strong way too quickly for generator to learn anything. Now there are some tricks typically recommended for that problem with