Image style transfer using convolutional neural networks based on transfer learning

Gupta, Vineeta; Sadana, Rajat; Moudgil, Swastikaa

doi:10.1504/ijcsyse.2019.098418

Cited by 18 publications

(3 citation statements)

References 2 publications

(2 reference statements)

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“…One popular approach involves manipulating the intermediate features of a CNN. The gram matrix was utilized for CNN's middle feature extraction from a pretrained VGG network to represent style [20][21][22][23][24][25][26][27][28]. Similarly, Holden et al proposed analogous ideas for motion stylization [29].…”

Section: Domain Adaptation and Stnsmentioning

confidence: 99%

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

Chatterjee,

Raju,

Selvamuthukumaran

et al. 2024

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According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively.

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Section: Domain Adaptation and Stnsmentioning

confidence: 99%

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

Chatterjee,

Raju,

Selvamuthukumaran

et al. 2024

Information

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“…Gupta and other research teams used transfer learning to pre train convolutional neural networks for image style transfer tasks. Using these models can generate high perceptual quality images, which are a combination of the content of any image and the appearance of famous artworks [12]. Kim and other scholars proposed a CNN inference accelerator for style transformation applications, which utilized network compression and layer chain technology.…”

Section: Related Workmentioning

confidence: 99%

Application of Style Transfer Algorithm in Artistic Design Expression of Terrain Environment

Chen

2024

IJACSA

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The use of artistic expression to depict and express terrain and landform can not only convey terrain information, but also spread art and culture. The existing landscape design methods focus on the accurate expression of terrain height and the realistic expression of form, but neglect the aesthetic aspect of landscape design. In view of this situation, this paper studied the use of generative adversarial network, constructed the presentation mode of landscape plane style, and realized the expression of landscape art style. A terrain style transfer model based on a pre-trained deep neural network model and style transfer algorithm was constructed to achieve a variety of terrain style expressions. The results showed that, in terms of Peak Signal to Noise Ratio, the proposed style transfer algorithm was higher than style attention network and adaptive instance normalization, and the peak signal-to-noise ratio index value was increased by 7.5% and 16.5%. This indicated that the style transfer model proposed by the topographic artistry research had more advantages in terms of image diversity and fidelity. The Structural Similarity Index of the proposed algorithm has been greatly improved. This research expands the method of computer rendering of terrain environment art, which is of great significance for the preservation of traditional Chinese culture.

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“…CNNs are inspired by the visual processing mechanism of the human brain and are particularly effective in tasks such as image recognition, object detection, and image segmentation [ 24 ]. The applications of CNN are extremely wide and include medical data analysis [ 25 , 26 ], autonomous vehicles [ 27 ], natural language processing [ 28 ], image style transfer [ 29 ], computational chemistry [ 30 ], and environmental monitoring control systems [ 31 ]. There is no direct relationship between CNNs and short-term memory (STM) in the cognitive sense.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory

Zimin,

Kazantsev,

Stasenko

2023

Biomimetics

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In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model’s performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model’s performance.

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Image style transfer using convolutional neural networks based on transfer learning

Cited by 18 publications

References 2 publications

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

Application of Style Transfer Algorithm in Artistic Design Expression of Terrain Environment

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory

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