  Existing deep learning-enabled semantic communication systems often rely on
shared background knowledge between the transmitter and receiver that includes
empirical data and their associated semantic information. In practice, the
semantic information is defined by the pragmatic task of the receiver and
cannot be known to the transmitter. The actual observable data at the
transmitter can also have non-identical distribution with the empirical data in
the shared background knowledge library. To address these practical issues,
this paper proposes a new neural network-based semantic communication system
for image transmission, where the task is unaware at the transmitter and the
data environment is dynamic. The system consists of two main parts, namely the
semantic coding (SC) network and the data adaptation (DA) network. The SC
network learns how to extract and transmit the semantic information using a
receiver-leading training process. By using the domain adaptation technique
from transfer learning, the DA network learns how to convert the data observed
into a similar form of the empirical data that the SC network can process
without retraining. Numerical experiments show that the proposed method can be
adaptive to observable datasets while keeping high performance in terms of both
data recovery and task execution.
