Deep neural networks (DNNs) are found to be vulnerable to adversarial attacks, and various methods have been proposed for the defense. Among these methods, adversarial training has been drawing increasing attention because of its simplicity and effectiveness. However, the performance of the adversarial training is greatly limited by the architectures of target DNNs, which often makes the resulting DNNs with poor accuracy and unsatisfactory robustness. To address this problem, we propose DSARA to automatically search for the neural architectures that are accurate and robust after adversarial training. In particular, we design a novel cell-based search space specially for adversarial training, which improves the accuracy and the robustness upper bound of the searched architectures by carefully designing the placement of the cells and the proportional relationship of the filter numbers. Then we propose a two-stage search strategy to search for both accurate and robust neural architectures. At the first stage, the architecture parameters are optimized to minimize the adversarial loss, which makes full use of the effectiveness of the adversarial training in enhancing the robustness. At the second stage, the architecture parameters are optimized to minimize both the natural loss and the adversarial loss utilizing the proposed multi-objective adversarial training method, so that the searched neural architectures are both accurate and robust. We evaluate the proposed algorithm under natural data and various adversarial attacks, which reveals the superiority of the proposed method in terms of both accurate and robust architectures. We also conclude that accurate and robust neural architectures tend to deploy very different structures near the input and the output, which has great practical significance on both hand-crafting and automatically designing of accurate and robust neural architectures.

Incorporating large-scale pre-trained models with the prototypical neural networks is a de-facto paradigm in few-shot named entity recognition. Existing methods, unfortunately, are not aware of the fact that embeddings from pre-trained models contain a prominently large amount of information regarding word frequencies, biasing prototypical neural networks against learning word entities. This discrepancy constrains the two models' synergy. Thus, we propose a one-line-code normalization method to reconcile such a mismatch with empirical and theoretical grounds. Our experiments based on nine benchmark datasets show the superiority of our method over the counterpart models and are comparable to the state-of-the-art methods. In addition to the model enhancement, our work also provides an analytical viewpoint for addressing the general problems in few-shot name entity recognition or other tasks that rely on pre-trained models or prototypical neural networks.

公平的聚类旨在将数据分为不同的簇，同时防止敏感属性（例如性别，种族，RNA测序技术），而不是主导聚类。尽管最近已经进行了许多作品并取得了巨大的成功，但其中大多数是启发式的，并且缺乏算法设计的统一理论。在这项工作中，我们通过开发一种相互信息理论来填补这一空白，以实现深度公平的聚类，并因此设计出一种称为FCMI的新型算法。简而言之，通过最大化和最大程度地减少共同信息，FCMI旨在通过深度公平的聚类（即紧凑，平衡和公平的簇）以及信息丰富的特征来实现四种特征。除了对理论和算法的贡献外，这项工作的另一个贡献是提出了一个基于信息理论的新颖的公平聚类指标。与现有的评估指标不同，我们的指标以整体而不是单独的方式来衡量聚类的质量和公平性。为了验证拟议的FCMI的有效性，我们对六个基准进行了实验，包括单细胞RNA-seq Atlas，而与11种最先进的方法相比，就五个指标而言。认可后将发布代码。

产品描述生成是一项具有挑战性且探索不足的任务。大多数这样的工作都采用一组产品属性，因为输入然后在单个通行证中从头开始生成描述。但是，在面对用户在约束描述时的动态愿望时，这种广泛的范式可能会受到限制，例如根据先前版本删除或添加用户指定属性的内容。为了应对这一挑战，我们在描述生成中探索了一种新的草稿编辑方式，从而导致了电子商务中提议的新任务控制文本编辑。更具体地说，我们允许系统从用户接收命令（删除或添加），然后通过基于上一个版本灵活修改内容来生成描述。通过修改以前的版本而不是从头开始，满足新需求更容易，更实用。此外，我们设计了一种数据增强方法，以纠正此任务中的低资源挑战，其中包含一种基于模型的基于规则的策略，以模仿人类的编辑。为了遵循这项新任务，我们介绍了一个人为编写的命令编辑数据集，称为e-cedits和一个新的指标“属性编辑”。我们的实验结果表明，在自动和人类评估中，使用新的数据增强方法在更大程度上优于基准。

面部去夹旨在从模糊的输入图像恢复清晰的面部图像，具有更明确的结构和面部细节。然而，大多数传统的图像和面部去夹方法的重点是整个产生的图像分辨率，而不考虑特殊的面部纹理并且通常产生无充气的细节。考虑到面部和背景具有不同的分布信息，在本研究中，我们设计了一种基于可分离的归一化和自适应非规范化（SnAdnet）的有效面部去孔网络。首先，我们微调面部解析网络以获得精确的面部结构。然后，我们将脸部解析功能划分为面部前景和背景。此外，我们构建了一种新的特征自适应非规范化，以将FAYCIAL结构规则为辅助的条件，以产生更加和谐的面部结构。另外，我们提出了一种纹理提取器和多贴片鉴别器，以增强所生成的面部纹理信息。 Celeba和Celeba-HQ数据集的实验结果表明，所提出的面部去孔网络以更具面部细节恢复面部结构，并在结构相似性索引方法（SSIM），峰值信号方面对最先进的方法进行有利的方法。信噪比（PSNR），Frechet Inception距离（FID）和L1以及定性比较。

当前的密集文本检索模型面临两个典型的挑战。首先，他们采用暹罗双重编码架构来独立编码查询和文档，以快速索引和搜索，同时忽略了较细粒度的术语互动。这导致了次优的召回表现。其次，他们的模型培训高度依赖于负面抽样技术，以在其对比损失中构建负面文档。为了应对这些挑战，我们提出了对抗猎犬速率（AR2），它由双重编码猎犬加上跨编码器等级组成。这两种模型是根据最小群体对手的共同优化的：检索员学会了检索负面文件以欺骗排名者，而排名者学会了对包括地面和检索的候选人进行排名，并提供渐进的直接反馈对双编码器检索器。通过这款对抗性游戏，猎犬逐渐生产出更难的负面文件来训练更好的排名者，而跨编码器排名者提供了渐进式反馈以改善检索器。我们在三个基准测试基准上评估AR2。实验结果表明，AR2始终如一地胜过现有的致密回收者方法，并在所有这些方法上实现了新的最新结果。这包括对自然问题的改进R@5％至77.9％（+2.1％），Triviaqa R@5％至78.2％（+1.4）和MS-Marco MRR@10％至39.5％（+1.3％）。代码和型号可在https://github.com/microsoft/ar2上找到。

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.

A crucial issue of current text generation models is that they often uncontrollably generate factually inconsistent text with respective of their inputs. Limited by the lack of annotated data, existing works in evaluating factual consistency directly transfer the reasoning ability of models trained on other data-rich upstream tasks like question answering (QA) and natural language inference (NLI) without any further adaptation. As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks. To alleviate this problem, we propose a weakly supervised framework that aggregates multiple resources to train a precise and efficient factual metric, namely WeCheck. WeCheck first utilizes a generative model to accurately label a real generated sample by aggregating its weak labels, which are inferred from multiple resources. Then, we train the target metric model with the weak supervision while taking noises into consideration. Comprehensive experiments on a variety of tasks demonstrate the strong performance of WeCheck, which achieves a 3.4\% absolute improvement over previous state-of-the-art methods on TRUE benchmark on average.