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DANICE: Domain adaptation without forgetting in neural image compression

机译：Danice：域适应而不忘记神经图像压缩

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摘要

Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC.
机译：神经图像压缩（NIC）是一种新的编码范式，其中编码能力被从数据学习的深层模型捕获。该数据驱动性质使新的潜在功能能够。在本文中，我们研究编解码器对感兴趣的定制域的适应性。我们表明NIC编解码器可转移，并且它们可以适用于相对较少的目标域图像。然而，天真的适应干扰了针对原始源域优化的解决方案，从而忘记该域中的原始编码能力，并且甚至可以与先前编码的比特流中断兼容性。解决这些问题，我们提出了Codec适应而不会忘记（CAWF），这是一种框架，可以通过添加少量自定义参数来避免这些问题，其中源编解码器在适应过程中保持嵌入和不变。实验证明了其有效性，并提供了对NIC灾难性干扰特征的有效见解。

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