GAN vs DCGAN difference - Data Science Stack Exchange
Jun 5, 2018 · Conv nets in general find areas of correlation within an image, that is, they look for spatial correlations. This means a DCGAN would likely be more fitting for image/video data, whereas the general idea of a GAN can be applied to wider domains, as the model specifics are left open to be addressed by individual model architectures.
Comparsion between DCGAN and WGAN - Data Science Stack …
Jul 4, 2019 · 2. DCGAN is more about network architecture alterations, while WGAN is an change to the loss function. There's nothing stopping you from using the DCGAN architecture with the WGAN objective function: all this means is minimizing an approximate Wasserstein loss, rather than a Jensen-Shannon divergence, using a particular network architecture.
Difference between simple GAN and DCGAN - Stack Overflow
The Deep Convolutional GAN (DCGAN) is another approche of GAN that is specially used for image data, the particulatity of DCGAN's is that they use convolution layers in the discriminator and transpose convolution layers for the generator. In my application i use convolution layers in the discriminator but rather than using transpose convolution ...
How to interpret the discriminator's loss and the generator's loss …
(the picture above is a result of the DCGAN rather than vanilla GAN) This is some common sense but still: like with most neural net structures tweaking the model, i.e. changing its parameters or/and architecture to fit your certain needs/data can improve the model or screw it.
pytorch - Difference between DCGAN & WGAN - Stack Overflow
May 31, 2020 · According to my experience, DCGAN proposed a well-tuned and simple model (or more specifically we can say it proposed a simple network structure with well-tuned optimizer) to generate images.WGAN proposed a new method of measuring the distance between data distribution and model distribution and theoretically solved the GAN's problem:unstable,mode …
DCGAN - advise on why the training is not working
May 21, 2021 · Deep-Learning-with-TensorFlow-2-and-Keras/Chapter 6/DCGAN.ipynb CIFAR10 DCGAN Example. How to Develop a GAN to Generate CIFAR10 Small Color Photographs. DC GAN with Batch Normalization not working. Problem. Apparently the training is not working generating images below (as the epoch progresses). Generated images. Training loss log
TensorFlow MNIST DCGAN: how to set up the loss function?
In a Keras DCGAN implementation the author used a little "workaround" for this problem: he simply built 3 models. The generator (G), the discriminator (D) and third one, where he just combined G with D, while setting the train-ability of D to false there.
Why is my DCGAN not converging? - Data Science Stack Exchange
Aug 16, 2022 · 1. I'm training a tf DCGAN on the MVTec hazelnut dataset and I found some difficulties. The problem is that after a lot of epochs the generate does not produce some quality images. My model is the following: def build_generator(self, noise_dim): # Input shape (None, 100) # Output shape (None, 64, 64, 3) model = Sequential(name = "Generator")
Tips & Tricks on training DCGAN on small dataset
Feb 8, 2018 · 1. I have made a DCGAN which I am trying to train on custom dataset of only 1200 images. I have tried to gather more, but even gathering these 1200 was hard enough. If you are wondering I used Google Chromes extension "Fakun Batch Download Image" to gather my dataset. TRAINING DETAILS: In training procedure I am simultaneously updating ...
How to Improve Image Quality in My DCGAN for Data …
Aug 2, 2024 · Thank you! Number of Epochs and Batch Size: I have experimented with increasing the number of epochs and adjusting the batch size, but this has not significantly improved the image quality. Learning Rate and Adam Parameters: I have tweaked the learning rate and the parameters b1 and b2 of the Adam optimizer, but I haven't seen much difference ...