  In Learned Image Compression (LIC), a model is trained at encoding and
decoding images sampled from a source domain, often outperforming traditional
codecs on natural images; yet its performance may be far from optimal on images
sampled from different domains. In this work, we tackle the problem of adapting
a pre-trained model to multiple target domains by plugging into the decoder an
adapter module for each of them, including the source one. Each adapter
improves the decoder performance on a specific domain, without the model
forgetting about the images seen at training time. A gate network computes the
weights to optimally blend the contributions from the adapters when the
bitstream is decoded. We experimentally validate our method over two
state-of-the-art pre-trained models, observing improved rate-distortion
efficiency on the target domains without penalties on the source domain.
Furthermore, the gate's ability to find similarities with the learned target
domains enables better encoding efficiency also for images outside them.
