Meta-Learning without Data via Unconditional Diffusion Models

Although few-shot learning aims to address data scarcity, it still requires large, annotated datasets for training, which are often unavailable due to cost and privacy concerns. Previous studies have utilized pre-trained diffusion models, either to synthesize auxiliary data besides limited labeled samples, or to employ diffusion models as zero-shot classifiers. However, they are limited to conditional diffusion models needing class prior information (
e.g
., carefully crafted text prompts) about unseen tasks. To overcome this, we leverage unconditional diffusion models
without needs for class information
to train a meta-model capable of generalizing to unseen tasks. The framework contains (1) a meta-learning without data approach that uses synthetic data during training; and (2) a diffusion model-based data augmentation to calibrate the distribution shift during testing. During meta-training, we implement a
self-taught
class-learner to gradually capture class concepts, guiding unconditional diffusion models to generate a
labeled
pseudo dataset. This pseudo dataset is then used to jointly train the class-learner and the meta-model, allowing for iterative refinement and clear differentiation between classes. During meta-testing, we introduce a data augmentation that employs the diffusion models used in meta-training, to narrow the gap between meta-training and meta-testing task distribution. This enables the meta-model trained on
synthetic
images to effectively classify
real
images in unseen tasks. Comprehensive experiments showcase the superiority and adaptability of our approach in four real-world scenarios. Code available at https://github.com/WalkerWorldPeace/MLWDUDM.