  Today, according to the Cisco Annual Internet Report (2018-2023), the
fastest-growing category of Internet traffic is machine-to-machine
communication. In particular, machine-to-machine communication of images and
videos represents a new challenge and opens up new perspectives in the context
of data compression. One possible solution approach consists of adapting
current human-targeted image and video coding standards to the use case of
machine consumption. Another approach consists of developing completely new
compression paradigms and architectures for machine-to-machine communications.
In this paper, we focus on image compression and present an inference-time
content-adaptive finetuning scheme that optimizes the latent representation of
an end-to-end learned image codec, aimed at improving the compression
efficiency for machine-consumption. The conducted experiments show that our
online finetuning brings an average bitrate saving (BD-rate) of -3.66% with
respect to our pretrained image codec. In particular, at low bitrate points,
our proposed method results in a significant bitrate saving of -9.85%. Overall,
our pretrained-and-then-finetuned system achieves -30.54% BD-rate over the
state-of-the-art image/video codec Versatile Video Coding (VVC).
