Title: Your Discriminative Model is Secretly a Generative Model
Primary area: interpretability and explainable AI
Keywords: Neural Tangent Kernel, Generative model

Abstract: Although discriminative and generative models are fundamentally equivalent in understanding data distributions, bridging these paradigms -- especially transforming off-the-shelf discriminative models into generative ones -- remains challenging. In this paper, we introduce a universal framework that unlocks the generative potential of any discriminative model by directly leveraging the data manifold encoded in its parameter space. Drawing inspiration from the score function used in diffusion models, which measures the distance between a sample and the data manifold in probability space, we generalize this concept to the functional domain. 
To achieve this, we introduce the Discriminative Score Function (DSF), which quantifies the functional distance between a sample and the data manifold by mapping both into a shared functional space using the Loss Tangent Kernel (LTK), a variant of the Neural Tangent Kernel.
Our framework is architecture- and algorithm-agnostic, as evidenced by various architectures such as ViT, ResNet, and DETR on tasks including object detection, classification, and self-supervised learning (\eg, CLIP and DINO). Additionally, our approach extends to applications in image editing, inpainting, and explainable AI (XAI).Finally, we demonstrate the promising potential of DSF by adopting diffusion model techniques for enhanced generation quality.

TL;DR: We show Discriminative models can serve as Generative model.