  Prototype-based meta-learning has emerged as a powerful technique for
addressing few-shot learning challenges. However, estimating a deterministic
prototype using a simple average function from a limited number of examples
remains a fragile process. To overcome this limitation, we introduce ProtoDiff,
a novel framework that leverages a task-guided diffusion model during the
meta-training phase to gradually generate prototypes, thereby providing
efficient class representations. Specifically, a set of prototypes is optimized
to achieve per-task prototype overfitting, enabling accurately obtaining the
overfitted prototypes for individual tasks. Furthermore, we introduce a
task-guided diffusion process within the prototype space, enabling the
meta-learning of a generative process that transitions from a vanilla prototype
to an overfitted prototype. ProtoDiff gradually generates task-specific
prototypes from random noise during the meta-test stage, conditioned on the
limited samples available for the new task. Furthermore, to expedite training
and enhance ProtoDiff's performance, we propose the utilization of residual
prototype learning, which leverages the sparsity of the residual prototype. We
conduct thorough ablation studies to demonstrate its ability to accurately
capture the underlying prototype distribution and enhance generalization. The
new state-of-the-art performance on within-domain, cross-domain, and few-task
few-shot classification further substantiates the benefit of ProtoDiff.
