Dual-Clustered Conditioning Toward GAN-Based Diverse Image Generation

Generative Artificial Intelligence (AI) has revolutionized image generation in the realm of consumer electronics, which has illustrated its significant impact on product development and user experiences. In this paper, we propose a class conditioned GAN with dual clustering to leverage correlations across both spatial and approximated discrete cosine transform (ADCT) domain towards improved diverse image generations. By analyzing the spectral bias from a frequency perspective through clustering in ADCT domain, the proposed achieves the advantage that class-conditioning provided by pixel clustering can be significantly strengthened and complemented by ADCT clustering. The sequential arrangement of ADCT clustering followed by pixel clustering not only optimizes their individual contribution and coordination, but also avoid the need to retrain the conditional generator and discriminator from scratch. Extensive experiments carried out illustrate that, in terms of FID and IS measurements as well as synthesized quality, integrity and diversity, our proposed achieves significant superiority against the existing state of the arts.