シナリオ
A data scientist training a GAN to generate synthetic data for augmentation.
この図に含まれるもの
図の背後にある意思決定を読み解く。
01
Whether to separate training of discriminator on real and fake data into distinct steps
02
Convergence criteria selection (loss threshold vs. fixed epochs)
03
Balancing generator and discriminator update frequencies per epoch
Use when designing or explaining GAN training workflows for synthetic data generation, especially in production pipelines.