Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

※ The user interface design of www.ieiespc.org has been recently revised and updated. Please contact inter@theieie.org for any inquiries regarding paper submission.

Journal Search

  1. Home
  2. Archive
  3. 2025-12 (Vol.14 No.06)
  4. 10.5573/IEIESPC.2025.14.6.764
XML PDF INFO REF

References

1 
Ramesh A., Dhariwal P., Nichol A., Chu C., Chen M., 2022, Hierarchical text-conditional image generation with CLIP latents, arXiv preprint arXiv.2204.06125DOI
2 
Saharia C., Chan W., Saxena S., Li L., Whang J., Denton E., 2022, Photorealistic text-to-image diffusion models with deep language understanding, arXiv preprint arXiv.2205.11487DOI
3 
Rombach R., Blattmann A., Lorenz D., Esser P., Ommer B., 2022, High-resolution image synthesis with latent diffusion modelsDOI
4 
Liu G., Reda F. A., Shih K. J., Wang T.-C., Tao A., Catanzaro B., 2018, Image inpainting for irregular holes using partial convolutions, Lecture Notes in Computer Science, pp. 89-105DOI
5 
Yu J., Lin Z., Yang J., Shen X., Lu X., Huang T., 2019, Free-form image inpainting with gated convolutionDOI
6 
Li C., Wand M., 2016, Precomputed real-time texture synthesis with Markovian generative adversarial networks, Lecture Notes in Computer Science, pp. 702-716DOI
7 
Ronneberger O., 2017, Invited talk: U-Net convolutional networks for biomedical image segmentation, Bildverarbeitung für die Medizin 2017, pp. 3-3DOI
8 
Wang S., Duan H., Ding H., Tan Y.-P., Yap K.-H., Yuan J., 2022, Learning transferable human-object interaction detector with natural language supervisionDOI
9 
Nichol A., Dhariwal P., Ramesh A., Shyam P., Mishkin P., McGrew B., 2021, GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models, arXiv preprint arXiv.2112.10741DOI
10 
Zhu J.-Y., Krähenbühl P., Shechtman E., Efros A. A., 2016, Generative visual manipulation on the natural image manifold, Lecture Notes in Computer Science, pp. 597-613DOI
11 
Xia W., Zhang Y., Yang Y., Xue J.-H., Zhou B., Yang M., 2022, GAN inversion: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 3, pp. 3121-3138DOI
12 
Raffel C., Shazeer N., Roberts A., Lee K., Sarang M., 2020, Exploring the limits of transfer learning with a unified text-to-text transformer, Journal of Machine Learning Research, Vol. 21, No. 140, pp. 1-67DOI
13 
Ho J., Jain A. N., Abbeel P., 2020, Denoising diffusion probabilistic models, arXiv preprint arXiv.2006.11239DOI
14 
Ho J., Saharia C., Chan W., Fleet D. J., Norouzi M., Salimans T., 2021, Cascaded diffusion models for high fidelity image generation, arXiv preprint arXiv.2106.15282DOI
15 
Dhariwal P., Nichol A., 2021, Diffusion models beat GANs on image synthesis, arXiv preprint arXiv.2105.05233DOI
16 
Lin T.-Y., Maire M., Belongie S., Bourdev L., Girshick R., 2014, Microsoft COCO: Common objects in context, Lecture Notes in Computer Science, pp. 740-755DOI
17 
Liu Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S., 2022, A ConvNet for the 2020sDOI
18 
Shorten C., Khoshgoftaar T. M., 2019, A survey on image data augmentation for deep learning, Journal of Big Data, Vol. 6, No. 1DOI
19 
Simonyan K., Zisserman A., 2015, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556DOI
20 
He K., Zhang X., Ren S., Sun J., 2015, Deep residual learning for image recognition, arXiv preprint arXiv.1512.03385DOI
21 
Tan M., Le Q. V., 2019, EfficientNet: Rethinking model scaling for convolutional neural networks, arXiv preprint arXiv.1905.11946DOI
22 
Bird J. J., Lotfi A., 2024, CIFAKE: Image classification and explainable identification of AI-generated synthetic images, IEEE Access, Vol. 12, pp. 15642-15650DOI
23 
Krizhevsky A., Hinton G., 2009, Learning multiple layers of features from tiny imagesGoogle Search
24 
Mittal H., Saraswat M., Bansal J. C., Nagar A., 2020, Fake-face image classification using improved quantum-inspired evolutionary-based feature selection method, pp. 989-995DOI
25 
Real and Fake Face Detection — kaggle https:// www.kaggle.com/ciplab/real-and-fake-facedetecti on, (Accessed on 01/15/2020)., KaggleURL
26 
Krizhevsky A., Sutskever I., Hinton G. E., 2012, Imagenet classification with deep convolutional neural networks, pp. 1097-1105Google Search
27 
Chen Y., Xia R., Yang K., Zou K., 2024, MFMAM: Image inpainting via multi-scale feature module with attention module, Computer Vision and Image Understanding, Vol. 238, pp. 103883DOI
28 
Corneanu C., Gadde R., Martinez A. M., 2024, Latentpaint: Image inpainting in latent space with diffusion modelsDOI
29 
Xie S., Zhang Z., Lin Z., Hinz T., Zhang K., 2023, Smartbrush: Text and shape guided object inpainting with diffusion modelDOI
30 
Wang X., Yu K., Dong C., Loy C. C., 2018, Recovering realistic texture in image super-resolution by deep spatial feature transformDOI
31 
Karras T., 2017, Progressive growing of GANs for improved quality, stability, and variationDOI
32 
Schuhmann C., Vencu R., Beaumont R., Kaczmarczyk R., Mullis C., Katta A., Coombes T., Jitsev J., Komatsuzaki A., 2021, LAION-400M : Open dataset of CLIP-filtered 400 million image-text pairs, arXiv preprint arXiv.2111.02114DOI