Title: Distribution-Aware Synergistic Evolution for Few-shot Discrimination and Generation
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Few-Shot Class Incremental Learning, Few-shot Image Generation, Open-set Learning, Stable Diffusion

Abstract: Discrimination and generation are two distinct yet complementary paradigms in machine learning.
Generally, discriminative models are better at estimating the $\textit{class center}$, while generative models are better at modeling the $\textit{data variance}$.
To harness the strengths of both paradigms, we propose a synergistic evolution framework that allows discriminative and generative methodologies to cooperate in estimating feature distributions.
For one thing, the discriminative model incorporates synthetic samples from the generative model to improve the estimation of feature covariance, especially when the available data is limited.
For another, the generative model leverages calibrated class centers from the discriminative pathway as anchors to improve the semantic accuracy of the generated samples.
In summary, our framework enables the discriminative model and the generative model to jointly develop and collaborate within a few-shot learning scenario, thereby enhancing both of their individual capabilities.
Additionally, our design improves open-set learning by enhancing out-of-distribution detection through better covariance modeling in the discriminative space. 
Extensive experiments on the CUB-200 and miniImageNet datasets demonstrate performance gains in few-shot class-incremental learning (FSCIL), few-shot incremental generation (FSIG), and open-set recognition tasks.