This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task. Unlike the commonly used autoregressive translation system, we adopted a two-stage translation paradigm called Hybrid Regression Translation (HRT) to combine the advantages of autoregressive and non-autoregressive translation. Specifically, HRT first autoregressively generates a discontinuous sequence (e.g., make a prediction every $k$ tokens, $k>1$) and then fills in all previously skipped tokens at once in a non-autoregressive manner. Thus, we can easily trade off the translation quality and speed by adjusting $k$. In addition, by integrating other modeling techniques (e.g., sequence-level knowledge distillation and deep-encoder-shallow-decoder layer allocation strategy) and a mass of engineering efforts, HRT improves 80\% inference speed and achieves equivalent translation performance with the same-capacity AT counterpart. Our fastest system reaches 6k+ words/second on the GPU latency setting, estimated to be about 3.1x faster than the last year's winner.

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions. Code is available at https://github.com/EndyWon/MicroAST.

近年来，基于注意力的场景文本识别方法非常受欢迎，并吸引了许多研究人员的兴趣。基于注意力的方法可以将注意力集中在解码过程中的小区域甚至单点上，其中注意矩阵几乎是一个旋转分布。此外，在推断过程中，所有注意力矩阵都将加权整个特征地图，从而导致巨大的冗余计算。在本文中，我们提出了一个用于场景文本识别的有效无注意的单点解码网络（称为SPDN），该网络可以取代传统的基于注意力的解码网络。具体而言，我们建议单点采样模块（SPSM）有效地在特征映射上为解码一个字符的一个关键点采样。这样，我们的方法不仅可以精确地找到每个字符的关键点，还可以删除冗余计算。基于SPSM，我们设计了一个高效且新颖的单点解码网络，以替代基于注意力的解码网络。对公开基准测试的广泛实验证明，我们的SPDN可以大大提高解码效率而不牺牲性能。

沟通效率在加速深神经网络（DNN）的分布式训练中起着重要作用。 All-Reduce是减少分布式DNN培训中模型参数的关键沟通原始性。大多数现有的全减少算法都是为传统的电气互连系统设计的，该系统无法满足大型DNN分布式培训的通信要求。电气互连的有希望的替代方案之一是光学互连，可以提供高带宽，低传输延迟和低功率成本。我们提出了一个称为WRHT（波长重复使用的层次树）的有效方案，用于在光学互连系统中实现全降压操作，该系统可以利用WDM（波长多路复用）来减少分布式数据 - 偏置DNN训练的通信时间。我们进一步得出了最少的通信步骤和通信时间，以实现使用WRHT的全面减少。仿真结果表明，与在光学互连系统中模拟的三种传统的全减少算法相比，WRHT的通信时间分别减少了75.59％，49.25％和70.1％。仿真结果还表明，与电气互连系统中的两种现有的全减速算法相比，WRHT可以将所有还原操作的通信时间减少86.69％和84.71％。

糖尿病性视网膜病（DR）和糖尿病黄斑水肿（DME）是全球永久失明的主要原因。在临床实践中设计具有良好泛化能力的自动分级系统至关重要。但是，先前的工作是独立的DR或DME等级，而无需考虑它们之间的内部相关性，或者通过共享特征表示共同对其进行分级，但忽略了由困难的样本和数据偏见引起的潜在概括问题。为了解决这些问题，我们提出了一个与动态难度意识的加权损失（DAW）和双流式分离的学习体系结构（分离）的框架。受课程学习的启发，DAW通过适应性地测量难度从简单的样本学习到困难样本。分离分离分级任务的特征，以避免潜在地强调偏见。通过添加DAW和Decarach，该模型学习了鲁棒的分离特征表示，以探索DR和DME之间的内部相关性并实现更好的分级性能。在三个基准测试的实验显示了我们框架内框架和跨数据库测试的有效性和鲁棒性。

在现实世界应用中的深度神经网络（DNN）的成功受益于丰富的预训练模型。然而，回溯预训练模型可以对下游DNN的部署构成显着的特洛伊木马威胁。现有的DNN测试方法主要旨在在对抗性设置中找到错误的角壳行为，但未能发现由强大的木马攻击所制作的后门。观察特洛伊木马网络行为表明，它们不仅由先前的工作所提出的单一受损神经元反射，而且归因于在多个神经元的激活强度和频率中的关键神经路径。这项工作制定了DNN后门测试，并提出了录音机框架。通过少量良性示例的关键神经元的差异模糊，我们识别特洛伊木马路径，特别是临界人，并通过模拟所识别的路径中的关键神经元来产生后门测试示例。广泛的实验表明了追索者的优越性，比现有方法更高的检测性能。通过隐秘的混合和自适应攻击来检测到后门的录音机更好，现有方法无法检测到。此外，我们的实验表明，录音所可能会揭示模型动物园中的模型的潜在潜在的背面。

虽然深入学习模型取得了前所未有的成功，但他们对逆势袭击的脆弱性引起了越来越关注，特别是在部署安全关键域名时。为了解决挑战，已经提出了鲁棒性改善的许多辩护策略，包括反应性和积极主动。从图像特征空间的角度来看，由于特征的偏移，其中一些人无法达到满足结果。此外，模型学习的功能与分类结果无直接相关。与他们不同，我们考虑基本上从模型内部进行防御方法，并在攻击前后调查神经元行为。我们观察到，通过大大改变为正确标签的神经元大大改变神经元来误导模型。受其激励，我们介绍了神经元影响的概念，进一步将神经元分为前，中间和尾部。基于它，我们提出神经元水平逆扰动（NIP），第一神经元水平反应防御方法对抗对抗攻击。通过强化前神经元并削弱尾部中的弱化，辊隙可以消除几乎所有的对抗扰动，同时仍然保持高良好的精度。此外，它可以通过适应性，尤其是更大的扰动来应对不同的扰动。在三个数据集和六种模型上进行的综合实验表明，NIP优于最先进的基线对抗11个对抗性攻击。我们进一步通过神经元激活和可视化提供可解释的证据，以便更好地理解。

在本文中，我们介绍了纹理改革器，一个快速和通用的神经基础框架，用于使用用户指定的指导进行交互式纹理传输。挑战在三个方面：1）任务的多样性，2）引导图的简单性，以及3）执行效率。为了解决这些挑战，我们的主要思想是使用由i）全球视图结构对准阶段，ii）局部视图纹理细化阶段和III）的新的前馈多视图和多级合成程序。效果增强阶段用相干结构合成高质量结果，并以粗略的方式进行细纹细节。此外，我们还介绍了一种新颖的无学习视图特定的纹理改革（VSTR）操作，具有新的语义地图指导策略，以实现更准确的语义引导和结构保存的纹理传输。关于各种应用场景的实验结果展示了我们框架的有效性和优越性。并与最先进的交互式纹理转移算法相比，它不仅可以实现更高的质量结果，而且更加显着，也是更快的2-5个数量级。代码可在https://github.com/endywon/texture --reformer中找到。

Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and detailed reading of the selected documents to generate answers. Retrievers and readers are usually modeled separately, which necessitates a cumbersome implementation and is hard to train and adapt in an end-to-end fashion. In this paper, we revisit this design and eschew the separate architecture and training in favor of a single Transformer that performs Retrieval as Attention (ReAtt), and end-to-end training solely based on supervision from the end QA task. We demonstrate for the first time that a single model trained end-to-end can achieve both competitive retrieval and QA performance, matching or slightly outperforming state-of-the-art separately trained retrievers and readers. Moreover, end-to-end adaptation significantly boosts its performance on out-of-domain datasets in both supervised and unsupervised settings, making our model a simple and adaptable solution for knowledge-intensive tasks. Code and models are available at https://github.com/jzbjyb/ReAtt.

Among current anchor-based detectors, a positive anchor box will be intuitively assigned to the object that overlaps it the most. The assigned label to each anchor will directly determine the optimization direction of the corresponding prediction box, including the direction of box regression and category prediction. In our practice of crowded object detection, however, the results show that a positive anchor does not always regress toward the object that overlaps it the most when multiple objects overlap. We name it anchor drift. The anchor drift reflects that the anchor-object matching relation, which is determined by the degree of overlap between anchors and objects, is not always optimal. Conflicts between the fixed matching relation and learned experience in the past training process may cause ambiguous predictions and thus raise the false-positive rate. In this paper, a simple but efficient adaptive two-stage anchor assignment (TSAA) method is proposed. It utilizes the final prediction boxes rather than the fixed anchors to calculate the overlap degree with objects to determine which object to regress for each anchor. The participation of the prediction box makes the anchor-object assignment mechanism adaptive. Extensive experiments are conducted on three classic detectors RetinaNet, Faster-RCNN and YOLOv3 on CrowdHuman and COCO to evaluate the effectiveness of TSAA. The results show that TSAA can significantly improve the detectors' performance without additional computational costs or network structure changes.