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Communication Dans Un Congrès Année : 2022

GANSet - generating annnotated datasets using generative adversarial networks

Résumé

The prediction of soil moisture for automated irrigation applications is a major challenge, as it is affected by various environmental parameters. The Application of Convolutional Neural Networks (CNN), to this end, has shown remarkable results for soil moisture prediction. These models, however, typically need large datasets, which are scarce in the agriculture field. To this end, this paper presents a Deep Convolutional Generative Adversarial Network (DCGAN) that can learn good data representations and generate highly realistic samples. Traditionally, Generative Adversarial Networks (GANs) have been used for generating data for segmentation and classification tasks or used in conjunction with CNNs or Multi Layer Perceptrons (MLPs) for regression tasks. In this paper, we propose a novel approach in which GANs are used to generate conjointly training images for plants as well as realistic regression values for their corresponding moisture levels without the use of any additional network. The generated images and regression vector targets, together with the training data, are then used to train a CNN which is then evaluated with actual test data from the dataset. We observe an improvement of error rate by 33 percent which shows the validity of our approach.
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Dates et versions

hal-03944297 , version 1 (17-01-2023)

Identifiants

Citer

Hajar Hammouch, Sambit Mohapatra, Mounim El Yacoubi, Huafeng Qin, Hassan Berbia, et al.. GANSet - generating annnotated datasets using generative adversarial networks. 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI), Nov 2022, Nanjing, China. pp.615-620, ⟨10.1109/ICCSI55536.2022.9970561⟩. ⟨hal-03944297⟩
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