Volterra Generative Models Introduce Fractional Noise for Score-Based Generation

Volterra generative models propose a continuous-time score-based framework using fractional kernels to inject path-dependent noise, avoiding memoryless noising in traditional diffusion models. The approach employs finite-dimensional Markovian lifts and demonstrates improved generation on MNIST and CIFAR-10, with a bridge sampler enhancing stability for larger models.