Overfitting for fun and profit: Instance-adaptive data compression

Ties van Rozendaal, Iris Huijben, Taco S. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

28 Downloads (Pure)


Neural data compression has been shown to outperform classical methods in terms of rate-distortion (RD) performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents. Due to limitations on model capacity and imperfect optimization and generalization, such models will suboptimally compress test data in general. However, one of the great strengths of learned compression is that if the test-time data distribution is known and relatively lowentropy (e.g. a camera watching a static scene, a dash cam in an autonomous car, etc.), the model can easily be finetuned or adapted to this distribution, leading to improved RD performance. In this paper we take this concept to the extreme, adapting the full model to a single video, and sending model updates (quantized and compressed using a parameter-space prior) along with the latent representation. Unlike previous work, we finetune not only the encoder/latents but the entire model, and - during finetuning - take into account both the effect of model quantization and the additional costs incurred by sending the model updates. We evaluate an image compression model on I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate that full-model adaptation improves RD performance by ∼ 1 dB, with respect to encoder-only finetuning.
Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Learning Representations, ICLR 2021
Number of pages18
Publication statusPublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Online
Duration: 3 May 20217 May 2021
Conference number: 9


Conference9th International Conference on Learning Representations, ICLR 2021
Abbreviated titleICLR 2021


Dive into the research topics of 'Overfitting for fun and profit: Instance-adaptive data compression'. Together they form a unique fingerprint.

Cite this