TensorFlow Machine Learning Projects

Challenges

Though GANs generate one of the sharpest images from a given piece of input data, their optimization is difficult to achieve due to unstable training dynamics. They also suffer from other challenges, such as mode collapse and bad initialization. Mode collapse is a phenomena where, if the data is multimodal, the generator is never incentivized to cover both modes, which leads to lower variability among generated samples and, hence, lower utility of GANs. If all generated samples start to become identical, it leads to complete collapse. In cases where most of the samples show some commonality, there is partial collapse of the model. At the core of this, GANs work on an objective function that aims to achieve optimization of min-max, ...