Meta-learning is used to establish the 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. Its function, focused solely on the logit, makes it deployable as an add-on to any existing classification procedure. All codes are hosted at the indicated link, https://github.com/limengyang1992/lpl.
Daily encounters with reflections from eyeglasses are commonplace, yet they are often detrimental to the quality of photographs. Current techniques for suppressing these unwanted noises utilize either correlated supplementary information or pre-determined prior conditions to confine this ill-posed problem. These techniques, unfortunately, are not adequately equipped to describe the nuances of reflections, rendering them ineffective in scenarios featuring strong and intricate reflections. By integrating image and hue information, this article proposes a hue guidance network (HGNet) with two branches for single image reflection removal (SIRR). The integration of pictorial aspects and color attributes has not been appreciated. The fundamental principle underlying this concept is our discovery that hue information precisely describes reflections, thus positioning it as a superior constraint for this specific SIRR task. Therefore, the leading branch pinpoints the significant reflection features by directly assessing the hue map. UC2288 concentration This secondary pathway exploits these powerful features, precisely locating vital reflective regions for achieving a high-quality reconstructed image. Additionally, a novel cyclic hue loss is engineered to guide network training toward a more accurate optimization. Our network's superior performance in generalizing across diverse reflection scenes is corroborated by experimental results, showcasing a clear qualitative and quantitative advantage over leading-edge methods currently available. For the source codes, navigate to this repository on GitHub: https://github.com/zhuyr97/HGRR.
Currently, the sensory assessment of food is mainly reliant on artificial sensory evaluation and machine perception, but the artificial sensory evaluation is heavily influenced by subjective factors, and machine perception has difficulty reflecting human emotional responses. For the purpose of differentiating food odors, a frequency band attention network (FBANet) for olfactory EEG was developed and described in this article. A study on olfactory EEG evoked responses was structured to collect olfactory EEG data, and this data underwent preprocessing procedures, including frequency-based filtering. Importantly, the FBANet framework incorporated frequency band feature mining and self-attention mechanisms. Frequency band feature mining effectively identified diverse multi-band EEG characteristics, and frequency band self-attention mechanisms seamlessly integrated these features to enable classification. In the end, the FBANet's performance was critically evaluated in light of other advanced models. The findings indicate that FBANet's performance exceeds that of the state-of-the-art techniques. In closing, FBANet's analysis successfully extracted information from olfactory EEG data, distinguishing between the eight food odors and proposing a new methodology for sensory evaluation through multi-band olfactory EEG.
Data in real-world applications frequently grows both in volume and the number of features it encompasses, a dynamic pattern over time. Moreover, they are usually gathered in collections, often called blocks. We label as blocky trapezoidal data streams data whose volume and features augment in a stepwise, block-like fashion. Current data stream models either fix the feature space or process single instances serially, thereby proving inadequate for dealing with the blocky trapezoidal form in data streams. Within this article, we introduce a novel algorithm for learning a classification model from blocky trapezoidal data streams, designated as learning with incremental instances and features (IIF). We are creating strategies for updating models dynamically, which can learn from the increasing amount of training data and the ever-expanding feature space. hand disinfectant Specifically, the data streams obtained in each round are initially divided, and then we build classifiers tailored to these separate divisions. We use a single global loss function to capture the relationships between classifiers, which enables effective information interaction between them. Ultimately, the final classification model relies on the methodology of ensemble learning. Additionally, to enhance its practicality, we translate this technique directly into a kernel approach. Empirical and theoretical analyses both confirm the efficacy of our algorithm.
Deep learning has played a crucial role in the advancement of hyperspectral image (HSI) classification methodologies. A significant shortcoming of many existing deep learning methods is their disregard for feature distribution, which can lead to the generation of poorly separable and non-discriminative features. From the perspective of spatial geometry, a superior feature distribution must fulfill both block and ring form criteria. A defining characteristic of this block is the tight clustering of intraclass instances and the substantial separation between interclass instances, all within the context of a feature space. The ring-shaped pattern signifies the overall distribution of class samples across a ring topology. Within this article, we introduce a novel deep ring-block-wise network (DRN) for HSI classification, considering the full extent of feature distribution. Within the DRN, a ring-block perception (RBP) layer is developed. This layer combines self-representation with ring loss within the perception model, ultimately creating the good distribution needed for high classification accuracy. This process mandates that the exported features meet the specifications of both the block and ring designs, resulting in a more separable and discriminatory distribution compared to traditional deep learning architectures. Additionally, we formulate an optimization strategy incorporating alternating updates to resolve this RBP layer model. Across the Salinas, Pavia Centre, Indian Pines, and Houston datasets, the proposed DRN method has consistently exhibited superior classification performance compared to current leading methodologies.
Recognizing a limitation in current convolutional neural network (CNN) compression techniques, which primarily target redundancy in a single dimension (e.g., spatial, temporal, or channel), this paper presents a novel multi-dimensional pruning (MDP) framework. This approach facilitates end-to-end compression of both 2-D and 3-D CNNs across multiple dimensions. The MDP model, in particular, indicates a simultaneous reduction of channels and an increased redundancy in supplementary dimensions. psycho oncology The impact of additional dimensions on Convolutional Neural Networks hinges on the nature of the input data. With images as input (2-D CNNs), the spatial dimension is critical; however, when processing videos (3-D CNNs), consideration must also be given to temporal dimensions, alongside spatial. To further extend our MDP framework, we introduce the MDP-Point approach, enabling the compression of point cloud neural networks (PCNNs) that process irregular point clouds (such as those used in PointNet). Along the supplementary dimension, the redundancy mirrors the count of points (that is, the number of points). Comprehensive experiments on six benchmark datasets reveal the effectiveness of our MDP framework in compressing CNNs, and its extension, MDP-Point, in compressing PCNNs.
Social media's rapid expansion has fundamentally reshaped the manner in which information travels, causing considerable problems for separating trustworthy news from unsubstantiated claims. Existing rumor detection approaches typically rely on the reposting dissemination of a potential rumor, framing reposts as a time-ordered sequence and learning the semantics within. Extracting useful backing from the topological layout of propagation and the sway of reposting authors in countering rumors is, however, critical, an area where existing methods generally fall short. Employing an ad hoc event tree approach, this article categorizes a circulating claim, extracting event components and converting it into a dual-perspective ad hoc event tree, one focusing on posts, the other on authors – thus enabling a distinct representation for the authors' tree and the posts' tree. Subsequently, we present a novel rumor detection model based on a hierarchical representation within bipartite ad hoc event trees, designated 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. By leveraging two public Twitter datasets, extensive experimentation demonstrates that BAET excels in exploring and exploiting rumor propagation structures, providing superior detection performance compared to existing baseline methods.
Cardiac segmentation from magnetic resonance imaging (MRI) scans is essential for analyzing the heart's anatomical and functional aspects, contributing to the assessment and diagnosis of cardiac conditions. While cardiac MRI produces hundreds of images per scan, the manual annotation process is complex and lengthy, thereby motivating the development of automatic image processing techniques. The proposed cardiac MRI segmentation framework, end-to-end and supervised, utilizes diffeomorphic deformable registration to segment cardiac chambers, handling both 2D and 3D image or volume inputs. 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.