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Fractal Few-Shot Learning | IEEE Journals & Magazine | IEEE Xplore

Fractal Few-Shot Learning


Abstract:

Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while imp...Show More

Abstract:

Forming deep feature embeddings is an effective method for few-shot learning (FSL). However, in the case of insufficient samples, overcoming the task complexity while improving the accuracy is still a major challenge. To address this problem, this article considers the consistency between similar data from the fractal perspective, introduces a priori knowledge, and proposes a fractal embedding model by combining FSL with fractal dimension theory for the first time. We improve the original fractal dimension algorithm used to describe image texture roughness to suit a neural network. Moreover, in accordance with the improved algorithm, prior knowledge of the quantized image is integrated into the features to reduce the impact of the data distribution on the model. Experimental results obtained on multiple image benchmark datasets show that the performance of the proposed model exceeds or matches that of previous state-of-the-art models. In addition, the proposed model achieves the best performance in cross-domain scenarios, further illustrating its robustness.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 35, Issue: 11, November 2024)
Page(s): 16353 - 16367
Date of Publication: 26 July 2023

ISSN Information:

PubMed ID: 37494172

Funding Agency:


References

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