Adapting Learned Image Compression Models to Multiple Domains with Supervised Decoder Adapters

แนวคิดหลัก

A novel method for adapting pre-trained learned image compression models to multiple target domains by plugging in domain-specific adapter modules into the decoder, without compromising performance on the source domain.

บทคัดย่อ

The paper proposes a method for domain adaptation in learned image compression (LIC) models. The key ideas are:

1. Plug-in K+1 adapter modules into the decoder of a pre-trained LIC model, where K are for target domains and 1 is for the source domain.
2. Train a gate network that predicts a probability distribution over the K+1 domains, which is used to blend the outputs of the adapters during decoding.
3. Train the adapters and gate jointly, while freezing the pre-trained encoder and decoder parameters.

This approach improves rate-distortion performance on the target domains without catastrophic forgetting on the source domain. It also enhances reconstruction quality for unseen image domains by leveraging the learned adapters.

The authors experiment with two state-of-the-art LIC models (Zou et al. and Cheng et al.) and demonstrate significant BD-Rate and BD-PSNR gains on the target sketch and comic domains compared to the reference pre-trained models. They also show improvements on unseen domains like infographics, drawings, and documents.

The proposed method is effective, efficient, and practical, as the adapters and gate do not modify the original pre-trained model parameters, allowing the original model to be used even if the adapters are unavailable during decoding.

สถิติ

The Kodak dataset has a BD-Rate of 0.0012 and BD-PSNR of ~0 for the Zou et al. model.
The CLIC dataset has a BD-Rate of 0.038 and BD-PSNR of ~0 for the Zou et al. model.
The Sketch dataset has a BD-Rate of -2.45 and BD-PSNR of 0.1718 for the Zou et al. model.
The Comic dataset has a BD-Rate of -4.93 and BD-PSNR of 0.28 for the Zou et al. model.

คำพูด

"Our method visibly improves the performance for both the target domains to the pre-trained model."
"Our method strikes rate reductions of 5% and 10% over the comic and sketch domains. Yet, we achieve some gain also over the source domain(Kodak and Clic datasets) for Cheng et al., proving our domain adaptation method does not incur in any catastrophic forgetting."
"For all domains the adopted training policy induces the exploitation of all the available adapters to increase the performance since; even though the gate is capable of identifying the predominant domain for an image, it still utilizes the others to a lesser extent."

ข้อมูลเชิงลึกที่สำคัญจาก

Domain Adaptation for Learned Image Compression with Supervised Adapters

by Alberto Pres... ที่ arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15591.pdf

สอบถามเพิ่มเติม

How can the proposed method be extended to handle a larger number of target domains without significantly increasing the model complexity

To handle a larger number of target domains without significantly increasing model complexity, the proposed method can be extended by implementing a more efficient adapter selection mechanism. Instead of creating a separate adapter for each target domain, a hierarchical adapter structure can be utilized. This structure can consist of primary adapters that are responsible for capturing general domain characteristics and secondary adapters that specialize in finer domain details. By organizing adapters in this hierarchical manner, the model can adapt to a larger number of target domains without a linear increase in complexity. Additionally, techniques like adapter sharing, where certain adapters are shared across related domains, can further reduce the overall model complexity while maintaining adaptability to diverse domains.

Can the gate network be trained in an unsupervised manner to eliminate the need for predefined adapter classes

The gate network can indeed be trained in an unsupervised manner to eliminate the need for predefined adapter classes. One approach is to leverage self-supervised learning techniques, where the gate network learns to predict the domain of an image based on inherent patterns and structures present in the data itself. By training the gate network in an unsupervised manner, it can autonomously identify domain-specific features and make informed decisions on how to blend the outputs of the adapters without relying on predefined classes. This unsupervised training can enhance the adaptability and generalization capabilities of the model, making it more robust to unseen domains and variations in data distribution.

What other techniques, such as modifying the entropy estimation, could be explored to further enhance the domain adaptation capabilities of learned image compression models

To further enhance the domain adaptation capabilities of learned image compression models, several techniques can be explored, including modifying the entropy estimation process. One approach is to incorporate adaptive entropy models that can dynamically adjust the compression parameters based on the characteristics of the input image. By adapting the entropy estimation to the specific domain or content of the image, the model can achieve better compression efficiency and reconstruction quality. Additionally, exploring advanced attention mechanisms, such as self-attention or multi-head attention, can help the model capture long-range dependencies and contextual information, improving its ability to adapt to diverse domains. Furthermore, integrating reinforcement learning techniques to optimize the adaptation process and fine-tune the model's performance on different domains can also be beneficial. By combining these approaches, the domain adaptation capabilities of learned image compression models can be further enhanced, leading to superior performance across a wide range of domains and data types.