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.

翻译：基于原型的小样本元学习已成为应对少样本学习挑战的强大技术。然而，基于有限样本使用简单平均函数来估计确定性原型仍是一个脆弱的过程。为克服这一局限，我们提出ProtoDiff，一种新颖框架，在元训练阶段利用任务引导的扩散模型逐步生成原型，从而提供高效的类别表示。具体而言，我们优化一组原型以实现每任务原型过拟合，从而精确获取各任务的过拟合原型。此外，我们在原型空间中引入任务引导的扩散过程，使元学习能够学习一个从普通原型向过拟合原型过渡的生成过程。在元测试阶段，ProtoDiff从随机噪声中逐步生成任务特定的原型，并基于新任务的有限样本进行条件生成。为加速训练并提升ProtoDiff性能，我们提出利用残差原型学习，利用残差原型的稀疏性。我们通过全面的消融研究，展示了其准确捕捉潜在原型分布并增强泛化能力的能力。在域内、跨域及少任务少样本分类任务上取得的最新最优性能，进一步证实了ProtoDiff的优势。