It is concluded that adversarial training is beneficial if and only if the reconstruction loss is not too constrained, and non-adversarial training outperforms (or is on par with) any method trained with a GAN when a constrained reconstruction loss is used in combination with batch normalisation.
This work proposes a new GAN-based face hallucination method primarily based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN), and presents a personalised adaptation of ESRGAN that employs the VGG16 architecture with a compact pre-trained version.
Sheetal S. Patil, A. Pawar, Nilofar Mulla et al.· International Journal of Eng...· 0 citations
Both numerical results and visual evaluations confirm that the proposed framework provides a balanced solution for preserving structural details while maintaining perceptual realism in image inpainting applications.
Simge Coşkun, A. Işık· Brain: Broad Research in Art...· 0 citations
This study builds and test a Deep Convolutional Generative Adversarial Network (DCGAN) that can produce realistic portraits of people's faces and proves that DCGANs are capable of creating realistic facial representations.
K. N. Reddy, A. Renuka· International Journal for Re...· 0 citations
This work proposes two novel attack methods targeted at detectors that leverage autoencoder reconstruction error and finds that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real.
R. Demchenko, Jonas Ricker, Asja Fischer· 0 citations
This study reveals an Asymmetric Adversarial Trajectory (AAT) property in LIC systems: transitioning from adversarial to benign regions is significantly easier than the reverse process, where adversarial examples can often be roughly recovered within only 1-2 steps.
Results indicate that LSGAN achieves a balanced performance between training stability and image fidelity, producing images with superior detail and realism, providing valuable guidance for GAN model selection in stylized image generation applications.
Bing-Hui He· Proceedings of the 3rd Inter...· 0 citations
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