Few-shot ship classification based on metric learning

Zhou, You; Chen, Changlin; Ma, Shukun

doi:10.1007/s00530-021-00847-w

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, most of the existing related studies in the literature are based on the randomly selected few data, without enough consideration of data information value. The related research works are mainly meta-learning, model fine-tuning, and applications (Karami et al, 2020 ; Nuthalapati and Tunga, 2021 ; Yang Y. et al, 2021 ; Zhou et al, 2021 ). Therefore, the small amount of data must be built on the premise of high quality to be meaningful.…”

Section: Introductionmentioning

confidence: 99%

Distance-Entropy: An Effective Indicator for Selecting Informative Data

Yang

Chao

2022

Front. Plant Sci.

View full text Add to dashboard Cite

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.

show abstract