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Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission.
C. E. Shannon, “A mathematical theory of communication,” The Bell system technical journal
1948
Earlier work this paper cites.
R. G. Gallager, “Low-density parity-check codes,” IRE Transactions on Information Theory
1963
Earlier work this paper cites.
C. A. Christopoulos, A. N. Skodras, and T. Ebrahimi, “The JPEG2000 still image coding system: An overview,” IEEE Trans. Consumer Electron
2000
Earlier work this paper cites.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003
2003
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” IEEE Transactions on information theory
2006
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems
2014
Earlier work this paper cites.
A. Mousavi, A. B. Patel, and R. G. Baraniuk, “A deep learning approach to structured signal recovery,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Bora, A. Jalal, E. Price, and A. G. Dimakis, “Compressed sensing using generative models,” in International Conference on Machine Learning
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” 2017
2017
Earlier work this paper cites.
M. Tschannen, E. Agustsson, and M. Lucic, “Deep generative models for distribution-preserving lossy compression,” Advances in neural information processing systems
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2018
Cited alongside, same era.
E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Trans. on Cognitive Commun. and Networking
2019
Cited alongside, same era.
K. Choi, K. Tatwawadi, A. Grover, T. Weissman, and S. Ermon, “Neural joint source-channel coding,” in International Conference on Machine Learning
2019
Cited alongside, same era.
C.-H. Lee, J.-W. Lin, P.-H. Chen, and Y.-C. Chang, “Deep learning-constructed joint transmission-recognition for internet of things,” IEEE Access
2019
Cited alongside, same era.
T. Marchioro, N. Laurenti, and D. Gündüz, “Adversarial networks for secure wireless communications,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2020
Later among the works it cites.
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, “Deep learning techniques for inverse problems in imaging,” IEEE Journal on Selected Areas in Information Theory
2020
Later among the works it cites.
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “PULSE: Self-supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the ieee/cvf conference on computer vision and pattern recognition
2020
Later among the works it cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems
2020
Later among the works it cites.
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2019
Cited alongside, same era.
Y. Blau and T. Michaeli, “Rethinking lossy compression: The rate-distortion-perception tradeoff,” in International Conference on Machine Learning
2019
Cited alongside, same era.
D. B. Kurka and D. Gündüz, “Joint source-channel coding of images with (not very) deep learning,” in International Zurich Seminar on Information and Communication (IZS 2020). Proceedings
2020
Cited alongside, same era.
D. B. Kurka and D. Gündüz, “DeepJSCC-f: Deep joint source-channel coding of images with feedback,” IEEE Journal on Selected Areas in Information Theory
2020
Cited alongside, same era.
M. Jankowski, D. Gündüz, and K. Mikolajczyk, “Joint device-edge inference over wireless links with pruning,” in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2020
Cited alongside, same era.
M. Jankowski, D. Gündüz, and K. Mikolajczyk, “Wireless image retrieval at the edge,” IEEE Journal on Selected Areas in Communications
2020
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of StyleGAN,” in Proc. CVPR
2020
Cited alongside, same era.
Y. M. Saidutta, A. Abdi, and F. Fekri, “VAE for joint source-channel coding of distributed Gaussian sources over AWGN MAC,” in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2020
Cited alongside, same era.
D. B. Kurka and D. Gündüz, “Bandwidth-agile image transmission with deep joint source-channel coding,” IEEE Transactions on Wireless Communications
2021
Later among the works it cites.
J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless image transmission using deep source channel coding with attention modules,” IEEE Transactions on Circuits and Systems for Video Technology
2021
Later among the works it cites.
G. Daras, J. Dean, A. Jalal, and A. Dimakis, “Intermediate layer optimization for inverse problems using deep generative models,” vol. 139, pp. 2421–2432, 18–24 Jul 2021
2021
Later among the works it cites.
2022
Closest in time.
T.-Y. Tung and D. Gündüz, “Deepwive: Deep-learning-aided wireless video transmission,” IEEE Journal on Selected Areas in Communications
2022
Closest in time.
M. Yang, C. Bian, and H.-S. Kim, “OFDM-guided deep joint source channel coding for wireless multipath fading channels,” IEEE Transactions on Cognitive Communications and Networking
2022
Closest in time.
D. Gündüz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,” IEEE Journal on Selected Areas in Communications
2022
Closest in time.
E. Erdemir, P. L. Dragotti, and D. Gündüz, “Privacy-aware communication over a wiretap channel with generative networks,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2022
Closest in time.