Image compression and denoising represent fundamental challenges in image
processing with many real-world applications. To address practical demands,
current solutions can be categorized into two main strategies: 1) sequential
method; and 2) joint method. However, sequential methods have the disadvantage
of error accumulation as there is information loss between multiple individual
models. Recently, the academic community began to make some attempts to tackle
this problem through end-to-end joint methods. Most of them ignore that
different regions of noisy images have different characteristics. To solve
these problems, in this paper, our proposed signal-to-noise ratio~(SNR) aware
joint solution exploits local and non-local features for image compression and
denoising simultaneously. We design an end-to-end trainable network, which
includes the main encoder branch, the guidance branch, and the signal-to-noise
ratio~(SNR) aware branch. We conducted extensive experiments on both synthetic
and real-world datasets, demonstrating that our joint solution outperforms
existing state-of-the-art methods.

我们提出的信噪比感知联合解决方案利用局部和非局部特征对图像压缩和去噪进行同时处理，通过设计一个端到端可训练的网络，它包括主编码器分支、引导分支和信噪比感知分支，我们在合成和真实数据集上进行了广泛实验，证明我们的联合解决方案优于现有的最先进方法。

强大的有损压缩技术对于噪声图像

Powerful Lossy Compression for Noisy Images

Bayesian optimization (BO) is a widely-used sequential method for
zeroth-order optimization of complex and expensive-to-compute black-box
functions. The existing BO methods assume that the function evaluation
(feedback) is available to the learner immediately or after a fixed delay. Such
assumptions may not be practical in many real-life problems like online
recommendations, clinical trials, and hyperparameter tuning where feedback is
available after a random delay. To benefit from the experimental
parallelization in these problems, the learner needs to start new function
evaluations without waiting for delayed feedback. In this paper, we consider
the BO under stochastic delayed feedback problem. We propose algorithms with
sub-linear regret guarantees that efficiently address the dilemma of selecting
new function queries while waiting for randomly delayed feedback. Building on
our results, we also make novel contributions to batch BO and contextual
Gaussian process bandits. Experiments on synthetic and real-life datasets
verify the performance of our algorithms.

本文研究了贝叶斯优化在随机延迟反馈问题上的表现，提出了具有子线性遗憾保证的算法，并在批量 BO 和上下文高斯过程老虎机方面做出了新贡献。作者在合成和真实数据集上进行了实验证明了算法的性能。