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Hybrid Fine-Tuning Strategy for Few-Shot Classification. | LitMetric

Hybrid Fine-Tuning Strategy for Few-Shot Classification.

Comput Intell Neurosci

Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Published: October 2022

AI Article Synopsis

  • Few-shot classification allows networks to learn feature extraction and label prediction with only a few labeled samples, but current methods struggle with tuning processes.
  • This study introduces a hybrid fine-tuning strategy (HFT) that combines a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT) to improve model accuracy and prevent overfitting.
  • Experiments on mini-ImageNet and tiered-ImageNet show that HFT outperforms existing fine-tuning methods across varying sample sizes and classification frameworks.

Article Abstract

Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569229PMC
http://dx.doi.org/10.1155/2022/9620755DOI Listing

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