Introduction to Label Space
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Label Space sentence examples within multi label classification
Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex.
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As the data size of Web-related multi-label classification problems continues to increase, the label space has also grown extremely large.
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Label Space sentence examples within multi label learning
Most multi-label learning algorithms ignore the potential distribution differences between the training domain and the test domain in the instance space and label space, as well as the intrinsic geometric information of the label space.
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In this paper, we proposed a new approach called multi-label learning with label-specific features using correlation information (LSF-CI) to learn label-specific features for each label with the consideration of both correlation information in label space and correlation information in feature space.
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Label Space sentence examples within Identical Label Space
Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications.
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While it has been studied for application in unsupervised person re-identification (ReID), the relations of feature distribution across the source and target domains remain underexplored, as they either ignore the local relations or omit the in-depth consideration of negative transfer when two domains do not share identical label spaces.
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Label Space sentence examples within Output Label Space
On the other hand, most feature learning studies often ignore the learning in the output label space, although taking advantage of label correlations can boost the classification performance.
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Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex.
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Label Space sentence examples within Original Label Space
Label Space sentence examples within Large Label Space
The main goal is to learn extreme classifier which extracts the subset of relevant labels from extremely large label space.
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It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step.
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Label Space sentence examples within Target Label Space
Label Space sentence examples within Noisy Label Space
However, the existing PML approaches always focus on leveraging the instance relationship to disambiguate the given noisy label space, while the potentially useful information in label space is not effectively explored.
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Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities.
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Label Space sentence examples within Level Label Space
Hash GCN and semantic GCN, which share parameters in the first two layers, propagate fusion information and generate hash codes under high-level label space supervision.
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Moreover, an effective View Correlation Discovery Network (VCDN) is proposed to further fuse the multi-view information in a higher-level label space.
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Label Space sentence examples within Different Label Space
This drives the transfer process with ascending difficulties, for addressing the difficulty from different label spaces, and ensuring the robustness of transfer.
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To address these issues, we propose a general Cross-modal Zero-shot Hashing (CZHash) solution to effectively leverage unlabeled and labeled multi-modality data with different label spaces.
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Label Space sentence examples within Semantic Label Space
However, it remains challenging to acquire novel classes in an online fashion for the segmentation task, mainly due to its continuously-evolving semantic label space, partial pixelwise ground-truth annotations, and constrained data availability.
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Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views.
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Label Space sentence examples within Clas Label Space
CLASR can not only build a scalable framework for adapting to multi-semantic embedding spaces, but also utilize the encoder-decoder paradigm for constraining the bidirectional projection between the feature space and the class label space.
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Ideally, supervised metric learning strategies learn a projection from a set of training data points so as to minimize intraclass variance while maximizing the interclass separability to the class label space.
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Label Space sentence examples within Shared Label Space
To overcome the lack of target labels in aligning geometries, this paper proposes learning the adaptive geometry that is derived from the domain-shared label space.
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While previous work on visual domain adaptation generally assumes discrete and shared label spaces, these assumptions are both invalid for pose estimation tasks.
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Label Space sentence examples within Continuou Label Space
Label Space sentence examples within Discrete Label Space
However, most existing methods based on convolutional neural networks aim to retrieve and classify affective images in a discrete label space while ignoring both the hierarchical and complex nature of emotions.
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To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.
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Label Space sentence examples within Source Label Space
Partial domain adaptation (PDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain based on an assumption that the source label space subsumes the target label space.
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How to effectively extract feature representations from unlabeled samples from the target domain is critical for unsupervised domain adaptation, specific for partial domain adaptation where source label space is a super-space of target label space, as it helps reduce large performance gap due to domain shift or domain bias.
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Label Space sentence examples within label space dimension
Some researches hence perform the Label Space Dimension Reduction(LSDR) to solve this problem, but a number of methods ignore the sequence information of texts and the label correlation in the original label space, and treat the label as a meaningless multi-hot vector.
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The novel proposed label space dimension reduction joint mutual information method (LSDR-JMI) is based on a filter multi-label feature selection algorithm.
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Label Space sentence examples within label space reduction
Label Space sentence examples within label space partition
The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity.
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In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework.
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Label Space sentence examples within label space mapping
To deal with this issue, the proposed method constructs an extended label space mapping to overcome the ``label space mismatching'' phenomenon; after that, the model of the undetected multitargets is established so that the tracks can be initialized outside the FoV of local sensors; finally and most important, weight selection and evolution mechanism are proposed such that the fusion weights are automatically tuned for each track at each time step and consensus step.
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Label Space sentence examples within label space sparsity
CM2AL first selects the most informative bag–label pairs by leveraging uncertainty, label correlations, label space sparsity, and informativeness from queried instances of the bag, and thus avoids scrutinizing all labels.
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CBMAL firstly selects a batch of informative instance-label pairs using uncertainty, label correlation and label space sparsity.
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We then project the latent representations to the label space for AD diagnosis.
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Specially, we make the distances of corresponding points in the projection subspaces as well as the label space close by Laplacian graph, which will guarantee the strictness of subspace structure and the quality of the pseudo labels.
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Also, if the label space is large, it contains few or no labeled instances for majority of the labels.
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After identifying the best combination of features, we applied 7 different multi-label models which are ML-kNN, MLTSVM and 5 Network-based Label Space Division (NLSD)-based methods (NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, NLSD-SVM).
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We analyze the theoretical properties of the recently proposed objective function for efficient online construction and training of multiclass classification trees in the settings where the label space is very large.
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Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification.
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In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed, (ii) unlabeled data is used, (iii) performance is improved, (iv) only a few labels are needed and (v) label spaces may differ.
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Then the non-occluded network is fixed and is used to guide the fine-tuning of the occluded network from both label space and feature space.
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The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity.
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Due to the exponential size of output space, exploiting intrinsic information in feature and label spaces has been the major thrust of research in recent years and use of parametrization and embedding have been the prime focus.
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As a two-step method, it first learns hash codes based on semantic labels, while preserving the similarity in the original space and exploiting the label correlations in the label space.
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As the dimensionality of label space increases, it becomes more difficult to deal with this kind of applications.
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Detecting tail-labels, which represent diversity of the label space and account for a large fraction (upto 80%) of all the labels, has been a significant research challenge in XMC.
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First, we propose a new triplet loss that allows distance ratios in the label space to be preserved in the learned metric space.
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Most supervised machine learning techniques, such as classification, rely on some underlying assumptions, such as: (a) the data distributions during training and prediction time are similar; (b) the label space during training and prediction time are similar; and (c) the feature space between the training and prediction time remains the same.
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As the label representations are explored from data, semantically similar categories will be assigned with the label representations that are close to each other in terms of Hamming distance in the label space.
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Finally, we map the learned latent representation into the label space.
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Concretely, we originally combine $\gamma$ -LSR with transfer learning to propose a novel knowledge and label space inductive transfer learning model for multiclass EEG signal recognition.
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In this paper, we approximate this measure using Moore-Penrose inverse matrix, linear kernel for feature space, and delta kernel for label space, and then symmetrize the entire matrix in the trace operation, resulting in an effective approximated and symmetrized representation.
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Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space.
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The spectral–spatial graph regularization and label space regularization are developed as the pixel-level constraints.
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Concretely, SFR can effectively reconstruct the high-resolution feature maps by recombining feature space, in which the space transformation matrix implicitly contained in a convolution layer can selectively highlight features at each position by leveraging the knowledge of label space in a self-learned way.
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, feature and label spaces), and locally linear embedding is employed to preserve the identical local geometric structure in different spaces.
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