2020/05/18 by Bingyu Liu, Zhen Zhao, Liu, Bingyu +9 · 25 citations
Computer Science · #Advanced Neural Network Applications #Artificial intelligence #Benchmark (surveying) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Domain Adaptation and Few-Shot Learning #Ensemble forecasting #Ensemble learning #FOS: Computer and information sciences #Feature (linguistics) #Feature extraction #Feature vector #Machine Learning and ELM #Machine learning #Pattern recognition (psychology) #Regularization (linguistics) #cs.CV
paper · pdf · doi:10.48550/arxiv.2005.08463
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/05/18 · arxiv created 2020/05/21 · arxiv updated 2020/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we proposes to construct an ensemble prediction model by performing diverse feature transformations after a feature extraction network. On each branch prediction network of the model we use a batch spectral regularization term to suppress the singular values of the feature matrix during pre-training to improve the generalization ability of the model. The proposed model can then be fine tuned in the target domain to address few-shot classification. We also further apply label propagation, entropy minimization and data augmentation to mitigate the shortage of labeled data in target domains. Experiments are conducted on a number of CD-FSL benchmark tasks with four target domains and the results demonstrate the superiority of our proposed model.