论文标题
具有批处理光谱正则化和数据混合的合奏模型，用于跨域的少量学习，没有标记的数据
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
论文作者
论文摘要
在本文中，我们介绍了我们提出的集合模型，其中包括跨域少数学习（CD-FSL）挑战的轨道2问题的批处理光谱正则化和数据混合机制。我们通过使用各种特征转换矩阵来构建一个多分支集成框架，同时在每个分支上部署批次光谱特征正规化以提高模型的可传递性。此外，我们提出了一种数据混合方法，以利用未标记的数据并增加目标域中设置的稀疏支持。我们提出的模型显示了CD-FSL基准任务的有效性能。
In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge. We build a multi-branch ensemble framework by using diverse feature transformation matrices, while deploying batch spectral feature regularization on each branch to improve the model's transferability. Moreover, we propose a data blending method to exploit the unlabeled data and augment the sparse support set in the target domain. Our proposed model demonstrates effective performance on the CD-FSL benchmark tasks.
