23,463 research outputs found
The adversarial joint source-channel problem
This paper introduces the problem of joint source-channel coding in the setup where channel errors are adversarial and the distortion is worst case. Unlike the situation in the case of stochastic source-channel model, the separation principle does not hold in adversarial setup. This surprising observation demonstrates that designing good distortion-correcting codes cannot be done by serially concatenating good covering codes with good error-correcting codes. The problem of the joint code design is addressed and some initial results are offered
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
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.Comment: AAAI202
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