We propose a new approach for non-Cartesian magnetic resonance image
reconstruction. While unrolled architectures provide robustness via
data-consistency layers, embedding measurement operators in Deep Neural Network
(DNN) can become impractical at large scale. Alternative Plug-and-Play (PnP)
approaches, where the denoising DNNs are blind to the measurement setting, are
not affected by this limitation and have also proven effective, but their
highly iterative nature also affects scalability. To address this scalability
challenge, we leverage the "Residual-to-Residual DNN series for high-Dynamic
range imaging (R2D2)" approach recently introduced in astronomical imaging.
R2D2's reconstruction is formed as a series of residual images, iteratively
estimated as outputs of DNNs taking the previous iteration's image estimate and
associated data residual as inputs. The method can be interpreted as a learned
version of the Matching Pursuit algorithm. We demonstrate R2D2 in simulation,
considering radial k-space sampling acquisition sequences. Our preliminary
results suggest that R2D2 achieves: (i) suboptimal performance compared to its
unrolled incarnation R2D2-Net, which is however non-scalable due to the
necessary embedding of NUFFT-based data-consistency layers; (ii) superior
reconstruction quality to a scalable version of R2D2-Net embedding an FFT-based
approximation for data consistency; (iii) superior reconstruction quality to
PnP, while only requiring few iterations.

基于非笛卡尔磁共振图像重建的 Residual-to-Residual DNN 系列 (R2D2) 方法，利用分层残差图像的迭代估计形成多层 DNN 输出，从而实现可扩展且优于传统 PnP 方法的重建质量。

可扩展的非笛卡尔磁共振成像方法: R2D2

Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2

Unfaithful text generation is a common problem for text generation systems.
In the case of Data-to-Text (D2T) systems, the factuality of the generated text
is particularly crucial for any real-world applications. We introduce R2D2, a
training framework that addresses unfaithful Data-to-Text generation by
training a system both as a generator and a faithfulness discriminator with
additional replacement detection and unlikelihood learning tasks. To facilitate
such training, we propose two methods for sampling unfaithful sentences. We
argue that the poor entity retrieval capability of D2T systems is one of the
primary sources of unfaithfulness, so in addition to the existing metrics, we
further propose NER-based metrics to evaluate the fidelity of D2T generations.
Our experimental results show that R2D2 systems could effectively mitigate the
unfaithful text generation, and they achieve new state-of-the-art results on
FeTaQA, LogicNLG, and ToTTo, all with significant improvements.

R2D2 是一种数据到文本生成的训练框架，它通过训练系统作为生成器和忠诚度鉴别器，加上替换检测和非似然性学习任务，来解决不忠实的数据到文本生成问题。我们还提出了两种采样不忠实句子的方法和 NER 基于的指标来评估 D2T 生成的保真度。实验结果表明，R2D2 系统能够有效缓解不忠实的文本生成，并在 FeTaQA、LogicNLG 和 ToTTo 上取得了新的最佳效果。