Visualize scatter plot with labels on each point
Tags: python, pandas, matplotlib, scatter-plot | Score: 1
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneousl…
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-s...
The Brandification of Private Labels: Beyond Budget
Mar 24, 2025 · Private labels, also known as store brands, were considered low-cost substitutes for decades—but their popularity is now booming. How and why is this transformation happening? What …
Extreme Zero-Shot Learning for Extreme Text Classification
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant labels for an input text instance from a large label set. However, the XMC setup faces two challenges: (1) it is not generalizable to predict unseen labels in dy...
UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
Existing multi-label ranking (MLR) frameworks only exploit information deduced from the bipartition of labels into positive and negative sets. Therefore, they do not benefit from ranking among positive labels, which is the novel MLR approach we intro...
GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking
Existing multi-label frameworks only exploit the information deduced from the bipartition of the labels into a positive and negative set. Therefore, they do not benefit from the ranking order between positive labels, which is the concept we introduce...
Font Generation with Missing Impression Labels
Our goal is to generate fonts with specific impressions, by training a generative adversarial network with a font dataset with impression labels. The main difficulty is that font impression is ambiguous and the absence of an impression label does not...
Haochen-Wang409/U2PL
[CVPR'22 & IJCV'24] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels & Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation (⭐ 474)
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Matching of Descriptive Labels to Glossary Descriptions
Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms, table column names) that are often consists of too short or too gene...
WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels
Compared to 2D lanes, real 3D lane data is difficult to collect accurately. In this paper, we propose a novel method for training 3D lanes with only 2D lane labels, called weakly supervised 3D lane detection WS-3D-Lane. By assumptions of constant lan...
Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs
The task of node classification is to infer unknown node labels, given the labels for some of the nodes along with the network structure and other node attributes. Typically, approaches for this task assume homophily, whereby neighboring nodes have s...
Keen Records - Wikipedia
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Sort-Invariant Non-Messing-Up
A poset has the non-messing-up property if it has two covering sets of disjoint saturated chains so that for any labeling of the poset, sorting the labels along one set of chains and then sorting the labels along the other set yields a linear extensi...
Universum Prescription: Regularization using Unlabeled Data
This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning. We call it universum prescription by the fact that the prescribed labels cannot be one of the supervi...
Representation Learning of Music Using Artist Labels
In music domain, feature learning has been conducted mainly in two ways: unsupervised learning based on sparse representations or supervised learning by semantic labels such as music genre. However, finding discriminative features in an unsupervised...
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under N…
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available themat...