BigGAN is a generative adversarial network designed to produce high-fidelity, high-resolution images by scaling up model and batch sizes. It incorporates techniques like class-conditional batch normalization and the hinge loss objective to enhance training stability and image quality. BigGAN has set new benchmarks in image synthesis, particularly with complex datasets like ImageNet, generating images up to 512×512 pixels with remarkable realism.