Meta-learning is employed to ascertain the appropriate augmentation, either regular or irregular, for each class. Extensive trials on both standard and long-tailed benchmark image classification datasets revealed the competitiveness of our learning approach. As its influence is confined to the logit output, it can be used as a readily adaptable module to merge with any existing classification algorithm. All the codes are downloadable from the following repository: https://github.com/limengyang1992/lpl.

The pervasive presence of reflections from eyeglasses in everyday life contrasts with their undesirable nature in photographic settings. These unwanted sounds are countered by methods that either exploit related supporting data or rely on user-defined prior knowledge to limit this ill-posed problem. These approaches, unfortunately, are hampered by their restricted capacity to detail the properties of reflections, which prevents them from handling complex and powerful reflection situations. Leveraging image and hue data, this article introduces the two-branched hue guidance network (HGNet) for the task of single image reflection removal (SIRR). The integration of pictorial aspects and color attributes has not been appreciated. This concept hinges on our conclusion that hue information provides an excellent representation of reflections, qualifying it as a superior constraint for the specific SIRR task. In this manner, the initial branch identifies the essential reflective properties by directly computing the hue map. medical audit By leveraging these substantial characteristics, the secondary branch facilitates the precise localization of prominent reflection regions, resulting in a high-fidelity reconstructed image. In parallel, a new method for cyclic hue loss is created to provide a more precise training optimization direction for the network. Experiments provide strong evidence for the superiority of our network, particularly its impressive generalization across various reflection settings, exhibiting a quantitative and qualitative advantage over current state-of-the-art approaches. Source code is accessible at the GitHub repository: https://github.com/zhuyr97/HGRR.

Food sensory appraisal now mostly hinges on artificial sensory evaluation and machine perception, yet artificial sensory evaluation is markedly susceptible to subjective biases, and machine perception has difficulty capturing the subtleties of human feelings. Using olfactory EEG data, this article proposes a frequency band attention network (FBANet) to identify and differentiate the nuances of various food odors. The olfactory EEG evoked experiment aimed to gather olfactory EEG data, and subsequent data preparation, such as frequency separation, was undertaken. The FBANet, composed of frequency band feature mining and self-attention modules, aimed to extract and integrate multi-band features from olfactory EEG. Frequency band feature mining effectively identified various features across different frequency ranges, while frequency band self-attention combined these diverse features for accurate classification. Lastly, a comparative analysis of the FBANet's performance was conducted relative to other advanced models. According to the results, FBANet outperformed the leading contemporary techniques. Finally, FBANet efficiently extracted and distinguished the olfactory EEG information associated with the eight food odors, suggesting a novel paradigm in food sensory evaluation based on multi-band olfactory EEG.

Many real-world applications encounter a continuous evolution of data, increasing in both its volume and the range of its features. Furthermore, these items are frequently gathered in groups (alternatively termed blocks). Blocky trapezoidal data streams are defined by the characteristic increase of their volume and features in discrete blocks. Stream analysis work often assumes a fixed feature space or processes data item-by-item; however, neither approach proves adequate for handling the blocky, trapezoidal structure of data streams. A newly proposed algorithm, learning with incremental instances and features (IIF), is introduced in this article to address the task of learning a classification model from blocky trapezoidal data streams. We endeavor to craft highly dynamic model update strategies capable of learning from an expanding dataset and a growing feature space. Immune infiltrate First, we divide the data streams collected in each round, and subsequently develop the appropriate classifiers for these distinct data partitions. To achieve efficient interaction of information between classifiers, a unifying global loss function is used to grasp their relationship. By employing the ensemble approach, the ultimate classification model is reached. Moreover, to make it more broadly applicable, we directly implement this technique as a kernel approach. The validity of our algorithm is confirmed through both theoretical and empirical assessments.

Deep learning has dramatically improved the accuracy of hyperspectral image (HSI) classification processes. Feature distribution is often overlooked by prevalent deep learning techniques, thereby producing features that are not easily distinguishable and lack the ability to discriminate effectively. In the domain of spatial geometry, a notable feature distribution design should satisfy the dual requirements of block and ring formations. In the feature space, the block is delineated by the closeness of intra-class samples and the vast separation of inter-class samples. The ring structure's pattern exemplifies the overall distribution of all class samples, conforming to a ring topology. To address HSI classification, we present a novel deep ring-block-wise network (DRN) in this article, considering the feature distribution comprehensively. The DRN's ring-block perception (RBP) layer, built upon integrating self-representation and ring loss, provides a well-distributed dataset, crucial for high classification performance. In this manner, the exported features are mandated to adhere to the specifications of both the block and the ring, leading to a more separable and discriminatory distribution compared to conventional deep networks. Moreover, we devise an optimization strategy, utilizing alternating updates, to ascertain the solution of this RBP layer model. The DRN method, as demonstrated by its superior classification results on the Salinas, Pavia Centre, Indian Pines, and Houston datasets, outperforms the current best-performing techniques.

In this work, we propose a multidimensional pruning (MDP) framework that contrasts with existing model compression techniques for convolutional neural networks (CNNs). These existing techniques generally focus on a single dimension of redundancy (e.g., channel, spatial, or temporal), whereas our approach compresses both 2-D and 3-D CNNs across multiple dimensions in an end-to-end fashion. Simultaneously reducing channels and increasing redundancy in other dimensions is a defining characteristic of MDP. learn more The relevance of extra dimensions within a Convolutional Neural Network (CNN) model hinges on the type of input data. Specifically, in the case of image inputs (2-D CNNs), it's the spatial dimension, whereas video inputs (3-D CNNs) involve both spatial and temporal dimensions. Our MDP framework is enhanced with the MDP-Point approach for compressing point cloud neural networks (PCNNs), specifically designed for irregular point clouds like those found in PointNet. The repeated nature of the extra dimension indicates the existence of points (i.e., the number of points). Extensive experimentation across six benchmark datasets highlights the efficacy of our MDP framework and its enhanced counterpart, MDP-Point, for compressing CNNs and PCNNs, respectively.

The meteoric rise of social media has had a considerable impact on the propagation of information, exacerbating the complexities of distinguishing authentic news from rumors. Rumor detection methods frequently leverage the reposting spread of potential rumors, treating all reposts as a temporal sequence and extracting semantic representations from this sequence. However, recognizing the topological patterns of spread and the role of reposting authors in debunking rumors remains vital, a weakness commonly exhibited by existing rumor-detection techniques. For this article, we organize a claim circulating as an ad hoc event tree, identifying event components and converting it to a bipartite ad hoc event tree with separate trees for posts and authors, yielding an author tree and a post tree. For this reason, we present a novel rumor detection model with a hierarchical structure applied to the bipartite ad hoc event trees, identified as BAET. The author word embedding and the post tree feature encoder are introduced, respectively, and a root-sensitive attention module is designed for node representation. We introduce a tree-like RNN model to capture structural correlations and a tree-aware attention module to learn tree representations, specifically for the author and post trees. Two public Twitter datasets reveal that BAET effectively charts rumor spread and outperforms baseline methods in detection, showcasing its superior performance.

Analyzing heart anatomy and function through magnetic resonance imaging (MRI) cardiac segmentation is vital for assessing and diagnosing heart diseases. Cardiac MRI scans yield a plethora of images per scan, hindering the feasibility of manual annotation, which in turn fuels the interest in automated image processing solutions. Employing a diffeomorphic deformable registration, this study presents a novel end-to-end supervised cardiac MRI segmentation framework that segments cardiac chambers from 2D and 3D image data or volumes. For precise representation of cardiac deformation, the method uses deep learning to determine radial and rotational components for the transformation, trained with a set of paired images and their segmentation masks. This formulation guarantees the invertibility of transformations and the prevention of mesh folding, thus ensuring the topological integrity of the segmentation results.