Multi-Instance Sketch to Image Synthesis With Progressive Generative Adversarial Networks

Wang, Zhi‐Hui; Wang, Ning; Shi, Junnan; Li, Jianjun; Yang, Hai-Rui

doi:10.1109/access.2019.2913178

Cited by 7 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using adversarial feedback, may directly supervise the network's hidden layers and improve the quality of the synthesis through the implicit iterative refining of the feature maps. The progressive adversarial networks [23] use a progressive adversarial learning framework to synthesize high-quality face photos from face sketches. The method involves training a series of generator networks and discriminator networks in a progressive manner, where each network is trained to generate photos of increasing resolution.…”

Section: Existing Adversarial Sketch-photo Transformation Methodsmentioning

confidence: 99%

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Shetty Kirimanjeshwara,

Narasimha Prasad

2024

IJECE

View full text Add to dashboard Cite

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

show abstract

Section: Existing Adversarial Sketch-photo Transformation Methodsmentioning

confidence: 99%

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Shetty Kirimanjeshwara,

Narasimha Prasad

2024

IJECE

View full text Add to dashboard Cite

show abstract

“…In the adversarial learning, 1,25–27 the discriminator and the generator form a two‐player game by min–max:

G^{*}, D^{*} = \arg \min_{G_{θ}} \max_{D_{ω}} L,

which is usually solved by alternating training. Specifically, in the process of alternate training, the parameter

ω

of the discriminator is first updated according to formula (3).…”

Section: Our Modelmentioning

confidence: 99%

A novel hybrid augmented loss discriminator for text‐to‐image synthesis

Gan

Liu

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

“…Liu et al [20] uses SR for human faces. In the general domains to generate images from sketches other than human faces, substantial advancements have been made [21,22,23,24,25,26]. More recently, Mask based image synthesis on human faces [27,28] is gaining attention.…”

Section: Related Workmentioning

confidence: 99%

Generator From Edges: Reconstruction of Facial Images

Takano¹,

Alaghband²

2020

Preprint

View full text Add to dashboard Cite

Applications that involve supervised training require paired images. Researchers of single image superresolution (SISR) create such images by artificially generating blurry input images from the corresponding ground truth. Similarly we can create paired images with the canny edge. We propose Generator From Edges (GFE) [Figure 2]. Our aim is to determine the best architecture for GFE, along with reviews of perceptual loss [1,2]. To this end, we conducted three experiments. First, we explored the effects of the adversarial loss often used in SISR. In particular, we uncovered that it is not an essential component to form a perceptual loss. Eliminating adversarial loss will lead to a more effective architecture from the perspective of hardware resource. It also means that considerations for the problems pertaining to generative adversarial network (GAN) [3], such as mode collapse, are not necessary. Second, we reexamined VGG loss and found that the mid-layers yield the best results. By extracting the full potential of VGG loss, the overall performance of perceptual loss improves significantly. Third, based on the findings of the first two experiments, we reevaluated the dense network to construct GFE. Using GFE as an intermediate process, reconstructing a facial image from a pencil sketch can become an easy task.

show abstract

Multi-Instance Sketch to Image Synthesis With Progressive Generative Adversarial Networks

Cited by 7 publications

References 31 publications

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

A novel hybrid augmented loss discriminator for text‐to‐image synthesis

Generator From Edges: Reconstruction of Facial Images

Contact Info

Product

Resources

About