In this work we present a novel optimization strategy for image
reconstruction tasks under analysis-based image regularization, which promotes
sparse and/or low-rank solutions in some learned transform domain. We
parameterize such regularizers using potential functions that correspond to
weighted extensions of the $\ell_p^p$-vector and $\mathcal{S}_p^p$
Schatten-matrix quasi-norms with $0 < p \le 1$. Our proposed minimization
strategy extends the Iteratively Reweighted Least Squares (IRLS) method,
typically used for synthesis-based $\ell_p$ and $\mathcal{S}_p$ norm and
analysis-based $\ell_1$ and nuclear norm regularization. We prove that under
mild conditions our minimization algorithm converges linearly to a stationary
point, and we provide an upper bound for its convergence rate. Further, to
select the parameters of the regularizers that deliver the best results for the
problem at hand, we propose to learn them from training data by formulating the
supervised learning process as a stochastic bilevel optimization problem. We
show that thanks to the convergence guarantees of our proposed minimization
strategy, such optimization can be successfully performed with a
memory-efficient implicit back-propagation scheme. We implement our learned
IRLS variants as recurrent networks and assess their performance on the
challenging image reconstruction tasks of non-blind deblurring,
super-resolution and demosaicking. The comparisons against other existing
learned reconstruction approaches demonstrate that our overall method is very
competitive and in many cases outperforms existing unrolled networks, whose
number of parameters is orders of magnitude higher than in our case.

该研究提出了一种新颖的优化策略，用于分析图像正则化下的图像重建任务，推动在一些学习转换域中稀疏和 / 或低秩解，并通过学习网络实现了较高性能。

迭代重新加权最小二乘网络：求解反向成像问题的收敛保证

Iterative Reweighted Least Squares Networks With Convergence Guarantees  for Solving Inverse Imaging Problems

We present a novel approach to robotic grasp planning using both a learned
grasp proposal network and a learned 3D shape reconstruction network. Our
system generates 6-DOF grasps from a single RGB-D image of the target object,
which is provided as input to both networks. By using the geometric
reconstruction to refine the the candidate grasp produced by the grasp proposal
network, our system is able to accurately grasp both known and unknown objects,
even when the grasp location on the object is not visible in the input image.
This paper presents the network architectures, training procedures, and grasp
refinement method that comprise our system. Experiments demonstrate the
efficacy of our system at grasping both known and unknown objects (91% success
rate in a physical robot environment, 84% success rate in a simulated
environment). We additionally perform ablation studies that show the benefits
of combining a learned grasp proposal with geometric reconstruction for
grasping, and also show that our system outperforms several baselines in a
grasping task.

本文介绍了一种基于学习抓取提案网络和学习三维形状重建网络的机器人抓取规划方法，使用几何重建来优化抓取提案网络生成的抓取点，实现了在图像中看不到抓取位置的情况下，对已知和未知物体进行准确抓取。实验结果表明，与其他模型相比，该方法在抓取任务中取得了更高的成功率。

基于图像抓取提案与三维重建的机器人抓取

Robotic Grasping through Combined Image-Based Grasp Proposal and 3D  Reconstruction

Deep reinforcement-learning methods have achieved remarkable performance on
challenging control tasks. Observations of the resulting behavior give the
impression that the agent has constructed a generalized representation that
supports insightful action decisions. We re-examine what is meant by
generalization in RL, and propose several definitions based on an agent's
performance in on-policy, off-policy, and unreachable states. We propose a set
of practical methods for evaluating agents with these definitions of
generalization. We demonstrate these techniques on a common benchmark task for
deep RL, and we show that the learned networks make poor decisions for states
that differ only slightly from on-policy states, even though those states are
not selected adversarially. Taken together, these results call into question
the extent to which deep Q-networks learn generalized representations, and
suggest that more experimentation and analysis is necessary before claims of
representation learning can be supported.

本篇论文重新审视了深度强化学习中表示学习的概念，并提出了几种定义，并在一个通用的测试任务上使用这些定义评估了算法的性能，结果表明代理在一些未被探索的状态下决策结果较差，这暗示我们需要更多的实验和分析，以支持表示学习的相关权利主张。