Few-shot Learning with LSSVM Base Learner and Transductive Modules
This work addresses the problem of improving few-shot classification accuracy, especially in 1-shot settings, for researchers and practitioners in meta-learning, though it is incremental as it builds on existing base learners and transductive methods.
The paper tackles few-shot learning by introducing a multi-class least squares support vector machine base learner for better generalization with less computational overhead and two transductive modules that modify the support set using query samples, achieving state-of-the-art performance on miniImageNet and CIFAR-FS benchmarks.
The performance of meta-learning approaches for few-shot learning generally depends on three aspects: features suitable for comparison, the classifier ( base learner ) suitable for low-data scenarios, and valuable information from the samples to classify. In this work, we make improvements for the last two aspects: 1) although there are many effective base learners, there is a trade-off between generalization performance and computational overhead, so we introduce multi-class least squares support vector machine as our base learner which obtains better generation than existing ones with less computational overhead; 2) further, in order to utilize the information from the query samples, we propose two simple and effective transductive modules which modify the support set using the query samples, i.e., adjusting the support samples basing on the attention mechanism and adding the prototypes of the query set with pseudo labels to the support set as the pseudo support samples. These two modules significantly improve the few-shot classification accuracy, especially for the difficult 1-shot setting. Our model, denoted as FSLSTM (Few-Shot learning with LSsvm base learner and Transductive Modules), achieves state-of-the-art performance on miniImageNet and CIFAR-FS few-shot learning benchmarks.