  We propose Conditional Idempotent Generative Networks (CIGN), a novel
approach that expands upon Idempotent Generative Networks (IGN) to enable
conditional generation. While IGNs offer efficient single-pass generation, they
lack the ability to control the content of the generated data. CIGNs address
this limitation by incorporating conditioning mechanisms, allowing users to
steer the generation process towards specific types of data.
  We establish the theoretical foundations for CIGNs, outlining their scope,
loss function design, and evaluation metrics. We then present two potential
architectures for implementing CIGNs: channel conditioning and filter
conditioning. Finally, we discuss experimental results on the MNIST dataset,
demonstrating the effectiveness of both approaches. Our findings pave the way
for further exploration of CIGNs on larger datasets and with more powerful
computing resources to determine the optimal implementation strategy.
