most citedLearned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IT2024

Neural Compress-and-Forward for the Relay Channel

Ezgi Ozyilkan, Fabrizio Carpi, Siddharth Garg +1

The relay channel, consisting of a source-destination pair and a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel rema…

cs.IT2024

Robust Distributed Compression with Learned Heegard-Berger Scheme

Eyyup Tasci, Ezgi Ozyilkan, Oguzhan Kubilay Ulger +1

We consider lossy compression of an information source when decoder-only side information may be absent. This setup, also referred to as the Heegard-Berger or Kaspi problem, is a s…

cs.IT2024

Distributed Compression in the Era of Machine Learning: A Review of Recent Advances

Ezgi Ozyilkan, Elza Erkip

Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. Whi…

cs.IT2023

Learned Wyner-Ziv Compressors Recover Binning

Ezgi Ozyilkan, Johannes Ballé, Elza Erkip

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case…

eess.IV20232 cited

Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines

Ezgi Ozyilkan, Mateen Ulhaq, Hyomin Choi +1

As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for…