基于对抗比特翻转的Infomax神经元联合信源-信道编码
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
摘要：
尽管香农理论指出将信源和信道编码分离为两个独立过程是渐进最优的，但在许多实际通信场景中，这种分解受到有限比特长度和解码计算能力的限制。最近，神经元联合信源-信道编码（NECST）被提出来解决这个问题.虽然它利用摊销推理和深度学习的进步来改进编码和解码过程，但由于其学习的编码网络的鲁棒性有限，它仍然不能总是在压缩和纠错性能方面取得令人信服的结果。基于神经元联合信源-信道编码与离散表示学习之间的内在联系，提出了一种新的正则化方法Infomax Adversarial-Bit-Flip（IABF）来提高神经元联合信源-信道编码方案的稳定性和鲁棒性。更具体地说，在编码器侧，我们建议显式地最大化码字和数据之间的互信息;而在解码器侧，摊销重建在对抗框架内进行正则化。在各种真实世界数据集上进行的大量实验证明，我们的IABF可以在压缩和纠错基准上实现最先进的性能，并且远远优于基线。
英文摘要：
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) is proposed to sidestep this problem. While it leverages the advancements of amortized inference and deep learning 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
