Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

在www.aicrowd.com平台上托管的学习竞赛自主赛车虚拟挑战由两个曲目组成：单摄像头和多相机。我们的Uniteam团队是单个相机轨道中的最终获胜者之一。该代理必须在最短时间内通过以前未知的F1风格轨道，而越野驾驶量最少。在我们的方法中，我们将U-NET体系结构用于道路细分，各种自动编码器编码道路二进制面具以及最近的邻居搜索策略，该策略选择给定状态的最佳动作。我们的经纪人在第1阶段（已知赛道）的平均速度为105 km/h，在第2阶段（未知轨道）上达到了73 km/h，而没有任何越野驾驶。在这里，我们提出解决方案和结果。代码实施可在此处提供：https：//gitlab.aicrowd.com/shivansh beohar/l2r

在这项工作中，我们展示了如何为来自单个混合音频通道的音频源分离问题调整公共可用的预先训练的Jukebox模型。我们的转移学习的神经网络架构快速训练，结果向其他最先进的方法表现出相当的性能。我们提供了我们体系结构的开源代码（https://rebrand.ly/transfer-jukebox-github）。

Named Entity Recognition (NER) is an important and well-studied task in natural language processing. The classic CoNLL-2003 English dataset, published almost 20 years ago, is commonly used to train and evaluate named entity taggers. The age of this dataset raises the question of how well these models perform when applied to modern data. In this paper, we present CoNLL++, a new annotated test set that mimics the process used to create the original CoNLL-2003 test set as closely as possible, except with data collected from 2020. Using CoNLL++, we evaluate the generalization of 20+ different models to modern data. We observe that different models have very different generalization behavior. F\textsubscript{1} scores of large transformer-based models which are pre-trained on recent data dropped much less than models using static word embeddings, and RoBERTa-based and T5 models achieve comparable F\textsubscript{1} scores on both CoNLL-2003 and CoNLL++. Our experiments show that achieving good generalizability requires a combined effort of developing larger models and continuing pre-training with in-domain and recent data. These results suggest standard evaluation methodology may have under-estimated progress on named entity recognition over the past 20 years; in addition to improving performance on the original CoNLL-2003 dataset, we have also improved the ability of our models to generalize to modern data.

We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that violate relevant policies. Our approach extracts structured representations of check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. Using our baseline system, we show that human fact-checkers can identify 124 tweets per hour that violate Twitter's policies on COVID-19 misinformation. We will make our code, data, and detailed annotation guidelines available to support the evaluation of human-in-the-loop systems that identify novel misinformation directly from raw user-generated content.

Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.

Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. Recently, a few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection by inserting special markers around the labeled spans in the original sentence. However, as far as we are aware, no empirical analysis has been conducted on how this approach compares to traditional annotation projection based on word alignment. In this paper, we present an extensive empirical study across 42 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. Experimental results show that our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods. We analyze several key factors that affect end-task performance, and show EasyProject works well because it can accurately preserve label span boundaries after translation. We will publicly release all our code and data.

Recent neural rendering approaches greatly improve image quality, reaching near photorealism. However, the underlying neural networks have high runtime, precluding telepresence and virtual reality applications that require high resolution at low latency. The sequential dependency of layers in deep networks makes their optimization difficult. We break this dependency by caching information from the previous frame to speed up the processing of the current one with an implicit warp. The warping with a shallow network reduces latency and the caching operations can further be parallelized to improve the frame rate. In contrast to existing temporal neural networks, ours is tailored for the task of rendering novel views of faces by conditioning on the change of the underlying surface mesh. We test the approach on view-dependent rendering of 3D portrait avatars, as needed for telepresence, on established benchmark sequences. Warping reduces latency by 70$\%$ (from 49.4ms to 14.9ms on commodity GPUs) and scales frame rates accordingly over multiple GPUs while reducing image quality by only 1$\%$, making it suitable as part of end-to-end view-dependent 3D teleconferencing applications. Our project page can be found at: https://yu-frank.github.io/lowlatency/.

Recent advances in coreset methods have shown that a selection of representative datapoints can replace massive volumes of data for Bayesian inference, preserving the relevant statistical information and significantly accelerating subsequent downstream tasks. Existing variational coreset constructions rely on either selecting subsets of the observed datapoints, or jointly performing approximate inference and optimizing pseudodata in the observed space akin to inducing points methods in Gaussian Processes. So far, both approaches are limited by complexities in evaluating their objectives for general purpose models, and require generating samples from a typically intractable posterior over the coreset throughout inference and testing. In this work, we present a black-box variational inference framework for coresets that overcomes these constraints and enables principled application of variational coresets to intractable models, such as Bayesian neural networks. We apply our techniques to supervised learning problems, and compare them with existing approaches in the literature for data summarization and inference.

我们介绍了一种新颖的深度学习方法，用于使用高分辨率的多光谱空中图像在城市环境中检测单个树木。我们使用卷积神经网络来回归一个置信图，指示单个树的位置，该位置是使用峰查找算法本地化的。我们的方法通过检测公共和私人空间中的树木来提供完整的空间覆盖范围，并可以扩展到很大的区域。在我们的研究区域，跨越南加州的五个城市，我们的F评分为0.735，RMSE为2.157 m。我们使用我们的方法在加利福尼亚城市森林中生产所有树木的地图，这表明我们有可能在前所未有的尺度上支持未来的城市林业研究。