SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator

• URL: http://arxiv.org/abs/2510.04576v1
• Date: Mon, 06 Oct 2025 08:26:06 GMT
• Title: SONA: Learning Conditional, Unconditional, and Mismatching-Aware Discriminator
• Authors: Yuhta Takida, Satoshi Hayakawa, Takashi Shibuya, Masaaki Imaizumi, Naoki Murata, Bac Nguyen, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuki Mitsufuji,
• Abstract summary: We introduce Sum of Naturalness and Alignment (SONA), which employs separate projections for naturalness (authenticity) and alignment in the final layer with an inductive bias.<n>Experiments on class-conditional generation tasks show thatSONA achieves superior sample quality and conditional alignment compared to state-of-the-art methods.
• Score: 54.562217603802075
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adversarial networks often struggle to balance the dual objectives of assessing authenticity and conditional alignment of input samples within their conditional discriminators. To address this, we propose a novel discriminator design that integrates three key capabilities: unconditional discrimination, matching-aware supervision to enhance alignment sensitivity, and adaptive weighting to dynamically balance all objectives. Specifically, we introduce Sum of Naturalness and Alignment (SONA), which employs separate projections for naturalness (authenticity) and alignment in the final layer with an inductive bias, supported by dedicated objective functions and an adaptive weighting mechanism. Extensive experiments on class-conditional generation tasks show that \ours achieves superior sample quality and conditional alignment compared to state-of-the-art methods. Furthermore, we demonstrate its effectiveness in text-to-image generation, confirming the versatility and robustness of our approach.

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