This video discusses training transformation models in machine learning. Unlike generative models, transformation models use input data to shape the output distribution. Key challenges include preventing the model from ignoring input data and handling paired vs. unpaired data. The video categorizes transformation tasks (one-to-one vs. one-to-many) and introduces approaches like Conditional GANs to incorporate input data information into the transformation process, improving learning stability even for standard generative tasks.