Deep learning-based methods have shown remarkable success for various image
restoration tasks such as denoising and deblurring. The current
state-of-the-art networks are relatively deep and utilize (variants of) self
attention mechanisms. Those networks are significantly slower than shallow
convolutional networks, which however perform worse. In this paper, we
introduce an image restoration network that is both fast and yields excellent
image quality. The network is designed to minimize the latency and memory
consumption when executed on a standard GPU, while maintaining state-of-the-art
performance. The network is a simple shallow network with an efficient block
that implements global additive multidimensional averaging operations. This
block can capture global information and enable a large receptive field even
when used in shallow networks with minimal computational overhead. Through
extensive experiments and evaluations on diverse tasks, we demonstrate that our
network achieves comparable or even superior results to existing
state-of-the-art image restoration networks with less latency. For instance, we
exceed the state-of-the-art result on real-world SIDD denoising by 0.11dB,
while being 2 to 10 times faster.

通过使用浅层网络和高效的块实现全局加性多维均值操作，我们介绍了一种图像复原网络，既具有快速执行速度又能提供优秀的图像质量。通过广泛的实验证明，我们的网络在各种任务上实现了与现有的最先进图像复原网络相媲美甚至更优秀的结果，并且具有较低的延迟。

GAMA-IR：全球累加多维均值用于快速图像恢复

GAMA-IR: Global Additive Multidimensional Averaging for Fast Image  Restoration

Large language models can produce powerful contextual representations that
lead to improvements across many NLP tasks. Since these models are typically
guided by a sequence of learned self attention mechanisms and may comprise
undesired inductive biases, it is paramount to be able to explore what the
attention has learned. While static analyses of these models lead to targeted
insights, interactive tools are more dynamic and can help humans better gain an
intuition for the model-internal reasoning process. We present exBERT, an
interactive tool named after the popular BERT language model, that provides
insights into the meaning of the contextual representations by matching a
human-specified input to similar contexts in a large annotated dataset. By
aggregating the annotations of the matching similar contexts, exBERT helps
intuitively explain what each attention-head has learned.

通过互动工具 exBERT，可以通过将人类指定的输入与大型已注释的数据集中的类似上下文进行匹配，来揭示上下文表示的意义，从而更直观地解释每个注意力头都学习了什么。