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Domain Adaptation for Learned Image Compression with Supervised Adapters
April 25, 2024, 7:45 p.m. | Alberto Presta, Gabriele Spadaro, Enzo Tartaglione, Attilio Fiandrotti, Marco Grangetto
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv compression cs.cv decoding domain domain adaptation domains eess.iv encoding image images natural performance type work
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