用自适应权重掩码提升生成对抗网络的少样本学习能力
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs
- 在生成器中引入残差单元，增强网络深度与样本质量
- 通过判别器权重掩码正则化，提升小样本类别特征学习能力
- 适用于数据稀缺场景，尤其适合快速适配新任务的模型
深度学习虽已革新多个领域，但在少样本学习中受限于过拟合和标注数据不足。本文提出一种基于残差权重掩码条件生成对抗网络（RWM-CGAN）的少样本学习方法，通过在生成器中引入残差单元以增强网络深度与样本质量，并在判别器中采用权重掩码正则化技术，提升小样本类别的特征学习能力。该方法有效扩展了样本空间，增强了生成样本的多样性和质量，显著提升了在公开数据集上的检测与分类准确率。实验表明，RWM-CGAN在数据稀缺条件下展现出更强的鲁棒性与泛化能力，为少样本学习提供了可行的解决方案。
原文摘要 · Abstract (English)
Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and the requirement of extensive annotated data, particularly in few-shot learning scenarios where limited samples are available. This paper introduces a novel approach to few-shot learning by employing a Residual Weight Masking Conditional Generative Adversarial Network (RWM-CGAN) for data augmentation. The proposed model integrates residual units within the generator to enhance network depth and sample quality, coupled with a weight mask regularization technique in the discriminator to improve feature learning from small-sample categories. This method addresses the core issues of robustness and generalization in few-shot learning by providing a controlled and clear augmentation of the sample space. Extensive experiments demonstrate that RWM-CGAN not only expands the sample space effectively but also enriches the diversity and quality of generated samples, leading to significant improvements in detection and classification accuracy on public datasets. The paper contributes to the advancement of few-shot learning by offering a practical solution to the challenges posed by data scarcity and the need for rapid generalization to new tasks or categories.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。