Partner-Assisted Learning for Few-Shot Image Classification
This addresses the problem of learning effective models with minimal annotation for few-shot classification, representing an incremental improvement over existing meta-learning approaches.
The paper tackles the challenge of training a feature extractor for few-shot image classification by proposing a two-stage training scheme called Partner-Assisted Learning (PAL), which consistently outperforms state-of-the-art methods on four benchmarks.
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to train a feature extractor is still a challenge. In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples. We propose a two-stage training scheme, Partner-Assisted Learning (PAL), which first trains a partner encoder to model pair-wise similarities and extract features serving as soft-anchors, and then trains a main encoder by aligning its outputs with soft-anchors while attempting to maximize classification performance. Two alignment constraints from logit-level and feature-level are designed individually. For each few-shot task, we perform prototype classification. Our method consistently outperforms the state-of-the-art method on four benchmarks. Detailed ablation studies of PAL are provided to justify the selection of each component involved in training.