Auxiliary Learning is a machine learning approach in which the model acknowledges the existence of objects that do not come under any of its learned categories.The name Auxiliary learning was chosen due to the introduction of an auxiliary class. The paper focuses on increasing the generality of existing narrow purpose neural networks and also highlights the need to handle unknown objects. The Cat & Dog binary classifier is taken as an example throughout the paper.

本文介绍了我们关于使用时间图像进行深泡探测的结果和发现。我们通过使用这些面部地标上的像素值构造图像（称为时间图像），模拟了在给定视频跨帧的468个面部标志物横跨给定视频框架中的临时关系。CNN能够识别给定图像的像素之间存在的空间关系。研究了10种不同的成像网模型。

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

随着超维数据的大数据分析的最新激增，对机器学习应用程序的降低技术的兴趣重新引起了人们的兴趣。为了使这些方法提高绩效提高并了解基础数据，需要确定适当的指标。此步骤通常被忽略，通常会选择指标，而无需考虑数据的基本几何形状。在本文中，我们提出了一种将弹性指标纳入T分布的随机邻居嵌入（T-SNE）和均匀的歧管近似和投影（UMAP）的方法。我们将方法应用于功能数据，该功能数据以旋转，参数化和比例为特征。如果这些属性被忽略，它们可能会导致不正确的分析和分类性能差。通过我们的方法，我们证明了三个基准数据集（MPEG-7，CAR数据集和Themoor的平面数据集）的形状识别任务的提高，我们分别获得了0.77、0.95和1.00 F1分数。

给定一个较小的培训数据集和学习算法，要达到目标验证或测试性能需要多少数据？这个问题至关重要，在诸如自动驾驶或医学成像之类的应用中，收集数据昂贵且耗时。高估或低估数据需求会带来大量费用，而预算可以避免。关于神经缩放定律的先前工作表明，幂律函数可以符合验证性能曲线并将其推断为较大的数据集大小。我们发现，这并不能立即转化为估计所需数据集大小以满足目标性能的更困难的下游任务。在这项工作中，我们考虑了一系列的计算机视觉任务，并系统地研究了一个概括功能功能的功能家族，以便更好地估算数据需求。最后，我们表明，结合调整的校正因子并在多个回合中收集会显着提高数据估计器的性能。使用我们的准则，从业人员可以准确估算机器学习系统的数据要求，以节省开发时间和数据采集成本。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

放射线学使用定量医学成像特征来预测临床结果。目前，在新的临床应用中，必须通过启发式试验和纠正过程手动完成各种可用选项的最佳放射组方法。在这项研究中，我们提出了一个框架，以自动优化每个应用程序的放射线工作流程的构建。为此，我们将放射线学作为模块化工作流程，并为每个组件包含大量的常见算法。为了优化每个应用程序的工作流程，我们使用随机搜索和结合使用自动化机器学习。我们在十二个不同的临床应用中评估我们的方法，从而在曲线下导致以下区域：1）脂肪肉瘤（0.83）； 2）脱粘型纤维瘤病（0.82）; 3）原发性肝肿瘤（0.80）; 4）胃肠道肿瘤（0.77）； 5）结直肠肝转移（0.61）; 6）黑色素瘤转移（0.45）; 7）肝细胞癌（0.75）; 8）肠系膜纤维化（0.80）; 9）前列腺癌（0.72）； 10）神经胶质瘤（0.71）; 11）阿尔茨海默氏病（0.87）;和12）头颈癌（0.84）。我们表明，我们的框架具有比较人类专家的竞争性能，优于放射线基线，并且表现相似或优于贝叶斯优化和更高级的合奏方法。最后，我们的方法完全自动优化了放射线工作流的构建，从而简化了在新应用程序中对放射线生物标志物的搜索。为了促进可重复性和未来的研究，我们公开发布了六个数据集，框架的软件实施以及重现这项研究的代码。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.