VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning

Jiawei Yan

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arXiv · 2604.23641

VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning

Jiawei Yan

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Abstract

This paper introduces VDLF-Net, which attaches a compact VAE to a multi-scale CNN backbone. Latent vectors and softmax-gate support the backbone feature maps, while $\ell_2$-normalized embeddings from the gated maps contribute toward supervised classification or episodic few-shot prediction. Under standard CIFAR-100 and Mini-ImageNet protocols, VDLF-Net demonstrates an improved performance over ResNet-50 Enhanced, VGG-16, Prototypical Networks, and Matching Networks. Extensive ablations show that removing the fine-resolution scale has the greatest impact on VDLF-Net's performance. At the same time, KL and reconstruction at the chosen $\alpha$ pose a minor performance reduction, demonstrating that performance gains over classical episodic baselines mainly originate from the full VDLF-Net architecture and training strategy.

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Jiawei Yan. 2026-04-26. VDLF-Net: Variational Feature Fusion for Adaptive and Few-Shot Visual Learning. https://arxiv.org/abs/2604.23641

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