Radar target recognition based on few-shot learning

Yue, Yang; Zhang, Zhuo; Mao, Wei; Li, Yang; Lv, Chen

doi:10.1007/s00530-021-00832-3

Cited by 23 publications

(13 citation statements)

References 36 publications

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“…In the image recognition competition ILSVRC held in 2012, AlexNet [1] won the championship by far surpassing the second place, the power of deep learning was finally shown in front of the world. With the continuous improvement of deep learning technology and hardware capabilities, artificial intelligence has developed more and more rapidly, and remarkable achievements have been made in many fields such as smart agriculture [2][3][4][5], medical treatment, finance, driverless, and so on [6][7][8][9][10][11][12]. Nowadays, more and more scholars begin to pay attention to how to apply in deep learning in the field of smart agriculture.…”

Section: Introductionmentioning

confidence: 99%

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

et al. 2022

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With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans’ rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development.

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Section: Introductionmentioning

confidence: 99%

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

et al. 2022

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“…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.

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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.

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“…However, it is impractical to capture both the clear and corresponding hazy image of the same scene simultaneously. One way to solve this issue is few-shot learning which complete training from a handful of data rather than millions of data [42][43][44][45][46]. Another way to fix this problem is using generative adversarial network and its variants [12,13,[18][19][20], among which models based on CycleGAN [21] is most prominent.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised single image dehazing with generative adversarial network

2022

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Most recent learning algorithms for single image dehazing are designed to train with paired hazy and corresponding ground truth images, typically synthesized images. Real paired datasets can help to improve performance, but are tough to acquire. This paper proposes an unsupervised dehazing algorithm based on GAN to alleviate this issue. An end-to-end network based on GAN architecture is established and fed with unpaired clean and hazy images, signifying that the estimation of atmospheric light and transmission is not required. The proposed network consists of three parts: a generator, a global test discriminator, and a local context discriminator. Moreover, a dark channel prior based attention mechanism is applied to handle inconsistency haze. We conduct experiments on RESIDE datasets. Extensive experiments demonstrated the effectiveness of the proposed approach which outperformed previous state-of-the-art unsupervised methods by a large margin.

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Radar target recognition based on few-shot learning

Cited by 23 publications

References 36 publications

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

A survey of few-shot learning in smart agriculture: developments, applications, and challenges

Distance-Entropy: An Effective Indicator for Selecting Informative Data

Unsupervised single image dehazing with generative adversarial network

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