Conditional Idempotent Generative Networks
Niccolò Ronchetti
TL;DR
This work extends Idempotent Generative Networks to Conditional Idempotent Generative Networks (CIGN), enabling single-pass, conditioned data generation. It builds a theoretical framework where conditioning is incorporated into an augmented data manifold $\widetilde{D}\subseteq X\times C$ and defines a five-term loss to enforce reconstruction, idempotence, and tightness for both matched and mismatched conditionings. The authors propose two realizations—channel conditioning and filter conditioning—and validate them on MNIST, showing that both approaches can produce quality class-conditioned digits, with large-channel conditioning delivering the strongest overall metrics. The study demonstrates that conditioning IGNs is feasible and effective, laying groundwork for scaling to larger datasets and guiding future comparisons of conditioning strategies.
Abstract
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.