bug and hurdle
-Mode Collapse: When the generator network generates a limited range of outputs, a common problem in GAN-based projects such as SRGAN is mode collapse, which frequently leads to unrealistic or repeating visuals.
-Training Stability: Convergence and consistently producing high-quality images can be challenging when using GAN training, which is infamously unstable.
-Hyperparameter tuning: It can be difficult and time-consuming to determine the ideal collection of hyperparameters, such as learning rates, batch sizes, and network topologies, for training the SRGAN model.
-Data Quantity and Quality: When dealing with a variety of real-world settings, the SRGAN model's performance may be impacted by the restricted availability of high-quality training data at various resolutions.
-Computational Resources: Not all developers may have easy access to the substantial computational resources needed for training intricate deep learning models like SRGAN, such as GPU acceleration.
solutions
-Architecture alterations: To address mode collapse and training instability issues, experiment with various architectural alterations, such as adding skip connections, modifying layer configurations, or employing different loss functions
-Advanced Training Methods: To stabilise GAN training and promote varied output generation, use advanced training methods such mini-batch discrimination, spectrum normalisation, or progressive growth strategies.
-Hyperparameter Search: To identify the ideal hyperparameter configurations for SRGAN training, systematically search for hyperparameters using methods such as grid search, random search, or Bayesian optimisation.
-Data augmentation: Using techniques like random cropping, rotation, flipping, or noise addition to improve the diversity and quality of the training data would increase the robustness of the SRGAN model.
-Transfer Learning: To speed up tha traning.