Partial Label Learning
27 papers with code • 11 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Partial Label Learning
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CIFAR-10
ISIC 2019
M-VAD Names
Most implemented papers
Exploiting Class Activation Value for Partial-Label Learning
As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels.
Towards Mitigating the Class-Imbalance Problem for Partial Label Learning
Partial label (PL) learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one is valid.
Progressive Identification of True Labels for Partial-Label Learning
Lvcrezia77/PRODEN
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ICML 2020
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
On the Power of Deep but Naive Partial Label Learning
We also address the question of how and why such a naive model works well with deep neural networks.
OPAM: Online Purchasing-behavior Analysis using Machine learning
To support the recent increase in online shopping trends, in this work, we present a customer purchasing behavior analysis system using supervised, unsupervised and semi-supervised learning methods.
Leveraged Weighted Loss for Partial Label Learning
hongwei-wen/LW-loss-for-partial-label
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As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
Contrastive Label Disambiguation for Partial Label Learning
hbzju/pico
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ICLR 2022
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Instance-Dependent Partial Label Learning
palm-ml/valen
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NeurIPS 2021
In this paper, we consider instance-dependent PLL and assume that each example is associated with a latent label distribution constituted by the real number of each label, representing the degree to each label describing the feature.
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
hbzju/pico
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Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Webly-Supervised Fine-Grained Recognition with Partial Label Learning
2023-MindSpore-1/ms-code-104
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IJCAI 2022
The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g., different bird species)by utilizing freely available but noisy web data. As the label noises signifcantly hurt the network training, it is desirable to distinguish and eliminate noisy images.
