One-shot Data Adaptive Semantic Communication for Image Transmission

Semantic communications rely on deep neural networks (DNNs) to reduce the amount of transmitted data by only transmitting the semantics of data rather than the whole data, showing the potential on image transmission even in low signal-to-noise-ratio (SNR) conditions. However, the performance deficiency happens once the real-time data do not follow the independent identical distribution (i.i.d) with the training dataset, which results from the poor generalization of DNNs. To tackle this problem, a promising solution is to align the real-time data to follow the similar distribution with the training dataset at the feature level. Thus, we propose a one-shot data adaptive semantic communication (ODASC), where domain adaptation is incorporated as a pre-processing module to cope with domain shift by aligning the distribution between the real-time data and the training dataset. Image transmission is considered as a case study to demonstrate the big plus of ODASC on data recovery and task accuracy.