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.

通过在解码器中插入适配器模块，我们解决了将预先训练的模型适应于多个目标领域的问题，并使用门网络从适配器中优化地融合贡献，实现了在目标领域上改进的码率失真和编码效率，同时在源领域上没有额外代价。

学习图像压缩的领域自适应与有监督适配器

Domain Adaptation for Learned Image Compression with Supervised Adapters

Machine learning models fail to perform well on real-world applications when
1) the category distribution P(Y) of the training dataset suffers from
long-tailed distribution and 2) the test data is drawn from different
conditional distributions P(X|Y). Existing approaches cannot handle the
scenario where both issues exist, which however is common for real-world
applications. In this study, we took a step forward and looked into the problem
of long-tailed classification under domain shifts. We designed three novel core
functional blocks including Distribution Calibrated Classification Loss,
Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation.
Furthermore, we adopted a meta-learning framework which integrates these three
blocks to improve domain generalization on unseen target domains. Two new
datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS. We
evaluated our method on the two datasets and extensive experimental results
demonstrate that our proposed method can achieve superior performance over
state-of-the-art long-tailed/domain generalization approaches and the
combinations. Source codes and datasets can be found at our project page
this https URL

使用三种新的核心功能块，以及集成在元学习框架中，解决长尾分布分类和领域漂移问题，提高了领域泛化在新的目标领域上的表现。

应对领域转移下的长尾类别分布

Tackling Long-Tailed Category Distribution Under Domain Shifts

In this paper, we tackle the domain adaptive object detection problem, where
the main challenge lies in significant domain gaps between source and target
domains. Previous work seeks to plainly align image-level and instance-level
shifts to eventually minimize the domain discrepancy. However, they still
overlook to match crucial image regions and important instances across domains,
which will strongly affect domain shift mitigation. In this work, we propose a
simple but effective categorical regularization framework for alleviating this
issue. It can be applied as a plug-and-play component on a series of Domain
Adaptive Faster R-CNN methods which are prominent for dealing with domain
adaptive detection. Specifically, by integrating an image-level multi-label
classifier upon the detection backbone, we can obtain the sparse but crucial
image regions corresponding to categorical information, thanks to the weakly
localization ability of the classification manner. Meanwhile, at the instance
level, we leverage the categorical consistency between image-level predictions
(by the classifier) and instance-level predictions (by the detection head) as a
regularization factor to automatically hunt for the hard aligned instances of
target domains. Extensive experiments of various domain shift scenarios show
that our method obtains a significant performance gain over original Domain
Adaptive Faster R-CNN detectors. Furthermore, qualitative visualization and
analyses can demonstrate the ability of our method for attending on the key
regions/instances targeting on domain adaptation. Our code is open-source and
available at https://github.com/Megvii-Nanjing/CR-DA-DET.

本研究提出一种基于分类正则化框架的域自适应目标检测方法，通过对图像级 / 实例级位移进行匹配以及对目标域关键图像区域的匹配减轻域位移，与传统的 Domain Adaptive Faster R-CNN 方法相比，实验证明该方法可以在各种域位移场景下取得显著的性能提升，且具有针对域自适应的关键区域能力。

探索分类正则化用于领域自适应目标检测

Exploring Categorical Regularization for Domain Adaptive Object  Detection

Transfer learning aims at improving the performance of target learners on
target domains by transferring the knowledge contained in different but related
source domains. In this way, the dependence on a large number of target domain
data can be reduced for constructing target learners. Due to the wide
application prospects, transfer learning has become a popular and promising
area in machine learning. Although there are already some valuable and
impressive surveys on transfer learning, these surveys introduce approaches in
a relatively isolated way and lack the recent advances in transfer learning.
Due to the rapid expansion of the transfer learning area, it is both necessary
and challenging to comprehensively review the relevant studies. This survey
attempts to connect and systematize the existing transfer learning researches,
as well as to summarize and interpret the mechanisms and the strategies of
transfer learning in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. Unlike previous
surveys, this survey paper reviews more than forty representative transfer
learning approaches, especially homogeneous transfer learning approaches, from
the perspectives of data and model. The applications of transfer learning are
also briefly introduced. In order to show the performance of different transfer
learning models, over twenty representative transfer learning models are used
for experiments. The models are performed on three different datasets, i.e.,
Amazon Reviews, Reuters-21578, and Office-31. And the experimental results
demonstrate the importance of selecting appropriate transfer learning models
for different applications in practice.

本综述对转移学习的 40 多个代表性方法进行了系统总结，从数据和模型的角度介绍了同质转移学习的机制和策略，并通过实验证明了在不同应用程序中选择适当的传输学习模型的重要性。