Adaptive neural architectures for multimodal content synthesis

Generative modeling has recently advanced to the point where synthesized content often appears realistic, but most existing models still have difficulty combining multiple data types—such as text, images, and audio—within a single framework. In this work, we introduce an adaptive neural architecture built around a gated Generative Adversarial Network (GAN) that addresses this gap by allowing flexible fusion of different input modalities. Our approach uses separate encoders for each modality, a trainable gating unit to control the contribution of each input, and a transformer-based fusion layer to better capture cross-modal relationships. We evaluated the model on MS-COCO, AudioSet, and VGGSound-Fusion benchmarks for tasks including text-to-image generation, image-to-audio synthesis, and multimodal content composition. Across these settings, our approach consistently outperformed prior methods, achieving up to 44% improvement in FID and 16% in CLIPScore. Interestingly, the model maintained strong performance even when some inputs were missing or corrupted, with less than 5% drop in accuracy in our ablation studies. These results indicate that an adaptive, gated GAN design can be a practical step toward more reliable and controllable multimodal content generation.