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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

February 1, 2023

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Authors

Yuxuan Song

Shanghai Jiao Tong University


Minkai Xu

Shanghai Jiao Tong University


Lantao Yu

Stanford University


Hao Zhou

Bytedance AI lab


Shuo Shao

Shanghai Jiao Tong University


Yong Yu

Shanghai Jiao Tong University


DOI:

10.1609/aaai.v34i04.6041


Abstract:

Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios this decomposition is limited by the finite bit-length and computational power for decoding. Recently, neural joint source-channel coding (NECST) (Choi et al. 2018) is proposed to sidestep this problem. While it leverages the advancements of amortized inference and deep learning (Kingma and Welling 2013; Grover and Ermon 2018) to improve the encoding and decoding process, it still cannot always achieve compelling results in terms of compression and error correction performance due to the limited robustness of its learned coding networks. In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. More specifically, on the encoder side, we propose to explicitly maximize the mutual information between the codeword and data; while on the decoder side, the amortized reconstruction is regularized within an adversarial framework. Extensive experiments conducted on various real-world datasets evidence that our IABF can achieve state-of-the-art performances on both compression and error correction benchmarks and outperform the baselines by a significant margin.

Topics: AAAI

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HOW TO CITE:

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip Proceedings of the AAAI Conference on Artificial Intelligence (2020) 5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip AAAI 2020, 5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu (2020). Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. Proceedings of the AAAI Conference on Artificial Intelligence, 5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. 2020. Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. "Proceedings of the AAAI Conference on Artificial Intelligence". 5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. (2020) "Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip", Proceedings of the AAAI Conference on Artificial Intelligence, p.5834-5841

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu, "Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip", AAAI, p.5834-5841, 2020.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. "Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. "Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 5834-5841.

Yuxuan Song||Minkai Xu||Lantao Yu||Hao Zhou||Shuo Shao||Yong Yu. Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip. AAAI[Internet]. 2020[cited 2023]; 5834-5841.


ISSN: 2374-3468


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