The problem of blind image super-resolution aims to recover high-resolution
(HR) images from low-resolution (LR) images with unknown degradation modes.
Most existing methods model the image degradation process using blur kernels.
However, this explicit modeling approach struggles to cover the complex and
varied degradation processes encountered in the real world, such as high-order
combinations of JPEG compression, blur, and noise. Implicit modeling for the
degradation process can effectively overcome this issue, but a key challenge of
implicit modeling is the lack of accurate ground truth labels for the
degradation process to conduct supervised training. To overcome this
limitations inherent in implicit modeling, we propose an
\textbf{U}ncertainty-based degradation representation for blind
\textbf{S}uper-\textbf{R}esolution framework (\textbf{USR}). By suppressing the
uncertainty of local degradation representations in images, USR facilitated
self-supervised learning of degradation representations. The USR consists of
two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a
feature extraction network composed of Variable Depth Dynamic Convolution
(VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR
images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with
Uncertainty Suppression Loss to suppress the inherent model uncertainty of the
Degradation Extractor. Furthermore, VDDC block integrates degradation
information through dynamic convolution. Rhe VDDC also employs an Adaptive
Intensity Scaling operation that adaptively adjusts the degradation
representation according to the network hierarchy, thereby facilitating the
effective integration of degradation information. Quantitative and qualitative
experiments affirm the superiority of our approach.

通过抑制图像中局部的退化表示的不确定性，提出了一个基于不确定性的退化表示的自监督学习方法，用于将低分辨率图像恢复为高分辨率图像。模型由两部分组成：自适应不确定性感知的退化提取（AUDE）和由可变深度动态卷积（VDDC）块组成的特征提取网络，实验证明该方法的优越性。

压制盲超分辨率中的不确定性

Suppressing Uncertainties in Degradation Estimation for Blind  Super-Resolution

Two desiderata of reinforcement learning (RL) algorithms are the ability to
learn from relatively little experience and the ability to learn policies that
generalize to a range of problem specifications. In factored state spaces, one
approach towards achieving both goals is to learn state abstractions, which
only keep the necessary variables for learning the tasks at hand. This paper
introduces Causal Bisimulation Modeling (CBM), a method that learns the causal
relationships in the dynamics and reward functions for each task to derive a
minimal, task-specific abstraction. CBM leverages and improves implicit
modeling to train a high-fidelity causal dynamics model that can be reused for
all tasks in the same environment. Empirical validation on manipulation
environments and Deepmind Control Suite reveals that CBM's learned implicit
dynamics models identify the underlying causal relationships and state
abstractions more accurately than explicit ones. Furthermore, the derived state
abstractions allow a task learner to achieve near-oracle levels of sample
efficiency and outperform baselines on all tasks.

用因果对等建模（CBM）方法在有因子的状态空间中学习动力学和奖励函数的因果关系，以得出最小的，任务特定的抽象。CBM 的隐式动力学模型可以在相同环境中重复使用，实验验证表明 CBM 的学习到的隐式动力学模型比显式模型更准确地识别了底层因果关系和状态抽象。此外，得出的状态抽象能够使任务学习者在所有任务上实现接近理想的样本效率，并在所有任务中优于基线模型。