基于监督适配器的学习图像压缩域适应
Domain Adaptation for Learned Image Compression with Supervised Adapters
AI总结：
本文提出一种基于监督适配器的学习图像压缩域适应方法，通过向解码器插入适配器模块并利用门控网络融合贡献，在保持源域性能的同时提升多目标域的率失真效率。
摘要：
在学习图像压缩（LIC）中，模型在编码和解码从源域采样的图像上进行训练，通常在自然图像上优于传统编解码器；然而，在从不同域采样的图像上，其性能可能远非最优。在这项工作中，我们通过向解码器中插入针对每个目标域（包括源域）的适配器模块，来解决将预训练模型适应到多个目标域的问题。每个适配器在不使模型遗忘训练时所见图像的情况下，提升了解码器在特定域上的性能。门控网络计算权重，以在解码比特流时最优地融合来自各适配器的贡献。我们在两个最先进的预训练模型上实验验证了我们的方法，观察到在目标域上率失真效率得到提升，且在源域上没有性能损失。此外，门控寻找与已学习目标域相似性的能力，也为域外图像实现了更好的编码效率。
英文摘要：
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 the decoder performance on a specific domain, without the model forgetting about the images seen at training time. A gate network computes the weights to optimally blend the contributions from the adapters when the bitstream is decoded. We experimentally validate our method over two state-of-the-art pre-trained models, observing improved rate-distortion efficiency on the target domains without penalties on the source domain. Furthermore, the gate's ability to find similarities with the learned target domains enables better encoding efficiency also for images outside them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
