Spatial frequency information fusion network for few-shot learning

TL;DR

This paper introduces SFIFNet, a novel few-shot learning model that integrates frequency domain information with spatial data to improve feature representation and classification accuracy in scenarios with limited data.

Contribution

The paper proposes an innovative data preprocessing method that combines frequency and spatial domain information to enhance few-shot learning performance.

Findings

SFIFNet outperforms existing models in classification accuracy.

Integrating frequency domain information improves feature representation.

The method effectively reduces overfitting in few-shot scenarios.

Abstract

The objective of Few-shot learning is to fully leverage the limited data resources for exploring the latent correlations within the data by applying algorithms and training a model with outstanding performance that can adequately meet the demands of practical applications. In practical applications, the number of images in each category is usually less than that in traditional deep learning, which can lead to over-fitting and poor generalization performance. Currently, many Few-shot classification models pay more attention to spatial domain information while neglecting frequency domain information, which contains more feature information. Ignoring frequency domain information will prevent the model from fully exploiting feature information, which would effect the classification performance. Based on conventional data augmentation, this paper proposes an SFIFNet with innovative data preprocessing. The key of this method is enhancing the accuracy of image feature representation by integrating frequency domain information with spatial domain information. The experimental results demonstrate the effectiveness of this method in enhancing classification performance.