One way to increase your dataset size for Pix2pix Gan is to use data augmentation techniques, such as flipping, rotating, and cropping images. This can help create new variations of your existing dataset.
While having a large dataset can improve the performance of Pix2pix Gan, it is not always necessary. You can still achieve good results with a smaller dataset by using techniques such as transfer learning.
To avoid overfitting, it is important to use a combination of data augmentation techniques and regularization methods. You can also split your dataset into training, validation, and testing sets to evaluate the performance of your model.
Yes, there are pre-trained models available for Pix2pix Gan that you can use as a starting point. These models have been trained on large datasets and can be fine-tuned to your specific dataset.
Yes, you can use a combination of real images and artificially generated images in your dataset for Pix2pix Gan. This can help improve the generalization of your model and make it more robust to real-world scenarios.