The latest advancements in neural image compression show great potential in
surpassing the rate-distortion performance of conventional standard codecs.
Nevertheless, there exists an indelible domain gap between the datasets
utilized for training (i.e., natural images) and those utilized for inference
(e.g., artistic images). Our proposal involves a low-rank adaptation approach
aimed at addressing the rate-distortion drop observed in out-of-domain
datasets. Specifically, we perform low-rank matrix decomposition to update
certain adaptation parameters of the client's decoder. These updated
parameters, along with image latents, are encoded into a bitstream and
transmitted to the decoder in practical scenarios. Due to the low-rank
constraint imposed on the adaptation parameters, the resulting bit rate
overhead is small. Furthermore, the bit rate allocation of low-rank adaptation
is \emph{non-trivial}, considering the diverse inputs require varying
adaptation bitstreams. We thus introduce a dynamic gating network on top of the
low-rank adaptation method, in order to decide which decoder layer should
employ adaptation. The dynamic adaptation network is optimized end-to-end using
rate-distortion loss. Our proposed method exhibits universality across diverse
image datasets. Extensive results demonstrate that this paradigm significantly
mitigates the domain gap, surpassing non-adaptive methods with an average
BD-rate improvement of approximately $19\%$ across out-of-domain images.
Furthermore, it outperforms the most advanced instance adaptive methods by
roughly $5\%$ BD-rate. Ablation studies confirm our method's ability to
universally enhance various image compression architectures.

通过低秩适应方法和动态适应网络，提出的神经图像压缩方法能够显著减小域差，并在多种图像压缩结构中普遍提升压缩效果。

通用神经图像压缩的动态低秩实例适应

Dynamic Low-Rank Instance Adaptation for Universal Neural Image  Compression

Several recent studies have shown that strong natural language understanding
(NLU) models are prone to relying on unwanted dataset biases without learning
the underlying task, resulting in models that fail to generalize to
out-of-domain datasets and are likely to perform poorly in real-world
scenarios. We propose two learning strategies to train neural models, which are
more robust to such biases and transfer better to out-of-domain datasets. The
biases are specified in terms of one or more bias-only models, which learn to
leverage the dataset biases. During training, the bias-only models' predictions
are used to adjust the loss of the base model to reduce its reliance on biases
by down-weighting the biased examples and focusing the training on the hard
examples. We experiment on large-scale natural language inference and fact
verification benchmarks, evaluating on out-of-domain datasets that are
specifically designed to assess the robustness of models against known biases
in the training data. Results show that our debiasing methods greatly improve
robustness in all settings and better transfer to other textual entailment
datasets. Our code and data are publicly available in
https://github.com/rabeehk/robust-nli.

本文提出了两种学习策略来训练神经模型，更加稳健，并且更能够迁移到域外的数据集。这些策略利用了数据集的偏差，通过 down-weighting 有偏差的例子，使基础模型减少对偏差的依赖。实验表明，我们的去偏差方法在所有设置中都显著提高了模型的稳健性，并且更好地迁移到其他文本蕴涵数据集上。