Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators
This addresses the limitation of existing few-shot learning methods that assume single-domain data, making it more practical for real-world applications.
The paper tackled the problem of few-shot classification across multiple domains, including unseen ones, by proposing a method that builds a pool of models with shared base networks and separate modulators, achieving improved performance on diverse datasets.
Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain. We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including ones never seen during meta-training. The key idea is to build a pool of models to cover this wide task distribution and learn to select the best one for a particular task through cross-domain meta-learning. All models in the pool share a base network while each model has a separate modulator to refine the base network in its own way. This framework allows the pool to have representational diversity without losing beneficial domain-invariant features. We verify the effectiveness of the proposed algorithm through experiments on various datasets across diverse domains.