对比性自我监督学习（CSL）是一种实用解决方案，它以无监督的方法从大量数据中学习有意义的视觉表示。普通的CSL将从神经网络提取的特征嵌入到特定的拓扑结构上。在训练进度期间，对比度损失将同一输入的不同视图融合在一起，同时将不同输入分开的嵌入。 CSL的缺点之一是，损失项需要大量的负样本才能提供更好的相互信息理想。但是，通过较大的运行批量大小增加负样本的数量也增强了错误的负面影响：语义上相似的样品与锚分开，因此降低了下游性能。在本文中，我们通过引入一个简单但有效的对比学习框架来解决这个问题。关键的见解是使用暹罗风格的度量损失来匹配原型内特征，同时增加了原型间特征之间的距离。我们对各种基准测试进行了广泛的实验，其中结果证明了我们方法在提高视觉表示质量方面的有效性。具体而言，我们使用线性探针的无监督预训练的Resnet-50在Imagenet-1K数据集上超过了受访的训练有素的版本。

为了在商店中充分利用计算机视觉技术，需要考虑适合零售场景特征的实际需求。为了实现这一目标，我们介绍了联合零售数据集（Unitail），这是针对检测，阅读和匹配算法的产品的基本视觉任务的大规模基准。凭借注释的180万个四边形实例，该Unitail提供了一个检测数据集，以更好地对齐产品外观。此外，它提供了一个包含1454个产品类别，30k文本区域和21k转录的画廊风格的OCR数据集，以实现对产品的强大阅读并激励增强的产品匹配。除了使用各种最新技术对数据集进行基准测试外，我们还定制了一个新的检测器以进行产品检测，并提供了一个简单的基于OCR的匹配解决方案，以验证其有效性。

Many real-world problems are inherently multimodal, from the communicative modalities humans use to express social and emotional states to the force, proprioception, and visual sensors ubiquitous on robots. While there has been an explosion of interest in multimodal representation learning, these methods are still largely focused on a small set of modalities, primarily in the language, vision, and audio space. In order to accelerate generalization towards diverse and understudied modalities, this paper studies efficient representation learning for high-modality scenarios. Since adding new models for every new modality or task becomes prohibitively expensive, a critical technical challenge is heterogeneity quantification: how can we measure which modalities encode similar information and interactions in order to permit parameter sharing with previous modalities? We propose two new information-theoretic metrics for heterogeneity quantification: (1) modality heterogeneity studies how similar 2 modalities $\{X_1,X_2\}$ are by measuring how much information can be transferred from $X_1$ to $X_2$, while (2) interaction heterogeneity studies how similarly pairs of modalities $\{X_1,X_2\}, \{X_3,X_4\}$ interact by measuring how much interaction information can be transferred from $\{X_1,X_2\}$ to $\{X_3,X_4\}$. We show the importance of these proposed metrics in high-modality scenarios as a way to automatically prioritize the fusion of modalities that contain unique information or interactions. The result is a single model, HighMMT, that scales up to $10$ modalities and $15$ tasks from $5$ different research areas. Not only does HighMMT outperform prior methods on the tradeoff between performance and efficiency, it also demonstrates a crucial scaling behavior: performance continues to improve with each modality added, and transfers to entirely new modalities and tasks during fine-tuning.

在基因组生物学研究中，调节基因组建模是许多监管下游任务的重要课题，例如推动者分类，交易因子结合位点预测。核心问题是模拟监管元素如何相互交互及其跨不同小区类型的可变性。然而，目前的深度学习方法通​​常专注于建模固定的细胞类型集的基因组序列，并且不考虑多个调节元件之间的相互作用，使它们仅在训练集中的小区类型上表现良好，并且缺乏所需的概括生物学应用。在这项工作中，我们提出了一种简单但有效的方法，用于以多模态和自我监督的方式预先培训基因组数据，我们称之为Genebert。具体而言，我们同时服用1D基因组数据和2D矩阵（转录因子X区）作为输入，其中提出了三项预训练任务，以提高模型的鲁棒性和概括性。我们在ATAC-SEQ数据集上预先培训我们的模型，具有1700万基因组序列。我们在不同细胞类型中评估我们的Genebert关于监管下游任务，包括启动子分类，交易因子结合位点预测，疾病风险估计和剪接部位预测。广泛的实验证明了大型监管基因组学数据的多模态和自我监督的预培训的有效性。

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.

We consider the inverse acoustic obstacle problem for sound-soft star-shaped obstacles in two dimensions wherein the boundary of the obstacle is determined from measurements of the scattered field at a collection of receivers outside the object. One of the standard approaches for solving this problem is to reformulate it as an optimization problem: finding the boundary of the domain that minimizes the $L^2$ distance between computed values of the scattered field and the given measurement data. The optimization problem is computationally challenging since the local set of convexity shrinks with increasing frequency and results in an increasing number of local minima in the vicinity of the true solution. In many practical experimental settings, low frequency measurements are unavailable due to limitations of the experimental setup or the sensors used for measurement. Thus, obtaining a good initial guess for the optimization problem plays a vital role in this environment. We present a neural network warm-start approach for solving the inverse scattering problem, where an initial guess for the optimization problem is obtained using a trained neural network. We demonstrate the effectiveness of our method with several numerical examples. For high frequency problems, this approach outperforms traditional iterative methods such as Gauss-Newton initialized without any prior (i.e., initialized using a unit circle), or initialized using the solution of a direct method such as the linear sampling method. The algorithm remains robust to noise in the scattered field measurements and also converges to the true solution for limited aperture data. However, the number of training samples required to train the neural network scales exponentially in frequency and the complexity of the obstacles considered. We conclude with a discussion of this phenomenon and potential directions for future research.

Maximum Inner Product Search (MIPS) is a popular problem in the machine learning literature due to its applicability in a wide array of applications, such as recommender systems. In high-dimensional settings, however, MIPS queries can become computationally expensive as most existing solutions do not scale well with data dimensionality. In this work, we present a state-of-the-art algorithm for the MIPS problem in high dimensions, dubbed BanditMIPS. BanditMIPS is a randomized algorithm that borrows techniques from multi-armed bandits to reduce the MIPS problem to a best-arm identification problem. BanditMIPS reduces the complexity of state-of-the-art algorithms from $O(\sqrt{d})$ to $O(\text{log}d)$, where $d$ is the dimension of the problem data vectors. On high-dimensional real-world datasets, BanditMIPS runs approximately 12 times faster than existing approaches and returns the same solution. BanditMIPS requires no preprocessing of the data and includes a hyperparameter that practitioners may use to trade off accuracy and runtime. We also propose a variant of our algorithm, named BanditMIPS-$\alpha$, which employs non-uniform sampling across the data dimensions to provide further speedups.

Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://github.com/ThrunGroup/FastForest.

Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have shown impressive results on various visual recognition tasks, alternating classic convolutional networks. While the initial patch-based models (ViTs) treated all patches equally, recent studies reveal that incorporating inductive bias like spatiality benefits the representations. However, most prior works solely focused on the location of patches, overlooking the scene structure of images. Thus, we aim to further guide the interaction of patches using the object information. Specifically, we propose OAMixer (object-aware mixing layer), which calibrates the patch mixing layers of patch-based models based on the object labels. Here, we obtain the object labels in unsupervised or weakly-supervised manners, i.e., no additional human-annotating cost is necessary. Using the object labels, OAMixer computes a reweighting mask with a learnable scale parameter that intensifies the interaction of patches containing similar objects and applies the mask to the patch mixing layers. By learning an object-centric representation, we demonstrate that OAMixer improves the classification accuracy and background robustness of various patch-based models, including ViTs, MLP-Mixers, and ConvMixers. Moreover, we show that OAMixer enhances various downstream tasks, including large-scale classification, self-supervised learning, and multi-object recognition, verifying the generic applicability of OAMixer

Trying to capture the sample-label relationship, conditional generative models often end up inheriting the spurious correlation in the training dataset, giving label-conditional distributions that are severely imbalanced in another latent attribute. To mitigate such undesirable correlations engraved into generative models, which we call spurious causality, we propose a general two-step strategy. (a) Fairness Intervention (FI): Emphasize the minority samples that are hard to be generated due to the spurious correlation in the training dataset. (b) Corrective Sampling (CS): Filter the generated samples explicitly to follow the desired label-conditional latent attribute distribution. We design the fairness intervention for various degrees of supervision on the spurious attribute, including unsupervised, weakly-supervised, and semi-supervised scenarios. Our experimental results show that the proposed FICS can successfully resolve the spurious correlation in generated samples on various datasets.