S2cGAN: Semi-Supervised Training of Conditional GANs with Fewer Labels

Chakraborty, Arunava Kumar; Ragesh, Rahul; Shah, Mitul; Kwatra, Nipun

doi:10.48550/arxiv.2010.12622

Cited by 1 publication

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 shows the differences between these two models. It may look similar, yet the major difference between them involves adding additional information to control the output [46,47]. So, the CGAN is an extension of the generative adversarial networks, which include a condition to both the generator (G) and discriminator (D) by feeding some extra information, y, into the input layer as an additional constraint.…”

Section: Cganmentioning

confidence: 99%

Adversarial Resolution Enhancement for Electrical Capacitance Tomography Image Reconstruction

Deabes

Abdel-Hakim

Bouazza

et al. 2022

Sensors

View full text Add to dashboard Cite

High-quality image reconstruction is essential for many electrical capacitance tomography (CT) applications. Raw capacitance measurements are used in the literature to generate low-resolution images. However, such low-resolution images are not sufficient for proper functionality of most systems. In this paper, we propose a novel adversarial resolution enhancement (ARE-ECT) model to reconstruct high-resolution images of inner distributions based on low-quality initial images, which are generated from the capacitance measurements. The proposed model uses a UNet as the generator of a conditional generative adversarial network (CGAN). The generator’s input is set to the low-resolution image rather than the typical random input signal. Additionally, the CGAN is conditioned by the input low-resolution image itself. For evaluation purposes, a massive ECT dataset of 320 K synthetic image–measurement pairs was created. This dataset is used for training, validating, and testing the proposed model. New flow patterns, which are not exposed to the model during the training phase, are used to evaluate the feasibility and generalization ability of the ARE-ECT model. The superiority of ARE-ECT, in the efficient generation of more accurate ECT images than traditional and other deep learning-based image reconstruction algorithms, is proved by the evaluation results. The ARE-ECT model achieved an average image correlation coefficient of more than 98.8% and an average relative image error about 0.1%.

show abstract

Section: Cganmentioning

confidence: 99%