GAN Training Data Generation for Rare-Event Segmentation

The method performs preliminary action by generating synthetic training data before the main training process. A first GAN is trained offline to generate diverse synthetic images including rare events, which are then used as training data for the second GAN. This preliminary data generation ensures rare modes are represented in the training set without complicating the main training procedure.

The first GAN acts as an intermediary that bridges the gap between limited real-world data and the need for diverse training samples. It generates synthetic data that mediates the training of the second GAN, allowing rare events to be represented without directly modifying the second GAN's training procedure.

The training process is segmented into two distinct phases: first, training a GAN to generate diverse synthetic data, and second, training the target model on this synthetic data. This segmentation allows the computationally intensive diversity generation to be performed once offline, while the main training proceeds efficiently on the prepared dataset.

The diverse data generation is performed as a preliminary step before the main training. By pre-generating synthetic training samples with rare events, the method avoids the need for time-consuming adaptive training during the main learning process, reducing overall training time.

Instead of collecting more real-world data, the method creates copies of common events with modified characteristics to represent rare events. The GAN learns from abundant real-world data and generates synthetic copies that capture rare patterns, making comprehensive dataset creation feasible without physical data collection.

The method changes parameters of existing data through the GAN's generative process. By modifying latent space representations and conditioning vectors, the GAN transforms common event data into rare event representations, achieving dataset diversity without collecting new physical data.