Improved Input Reprogramming for GAN Conditioning

Understand

We study the GAN conditioning problem, whose goal is to convert a pretrained unconditional GAN into a conditional GAN using labeled data.

• We first identify and analyze three approaches to this problem -- conditional GAN training from scratch, fine-tuning, and input reprogramming.
• Our analysis reveals that when the amount of labeled data is small, input reprogramming performs the best.
• Motivated by real-world scenarios with scarce labeled data, we focus on the input reprogramming approach and carefully analyze the existing algorithm.