  End-to-end trainable models have reached the performance of traditional
handcrafted compression techniques on videos and images. Since the parameters
of these models are learned over large training sets, they are not optimal for
any given image to be compressed. In this paper, we propose an instance-based
fine-tuning of a subset of decoder's bias to improve the reconstruction quality
in exchange for extra encoding time and minor additional signaling cost. The
proposed method is applicable to any end-to-end compression methods, improving
the state-of-the-art neural image compression BD-rate by $3-5\%$.
