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Found 8 papers, 2 papers with code
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlabeled data to build a predictive model.
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples.
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects.
Instance-Dependent Noisy Label Learning via Graphical Modelling
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them.
Ranked #1 on
Learning with noisy labels
on CIFAR-100
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space
Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes.
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces
In the context of GZSL, semantic information is non-visual data such as a text description of both seen and unseen classes.
Multi-modal Ensemble Classification for Generalized Zero Shot Learning
In this paper, we mitigate these issues by proposing a new GZSL method based on multi-modal training and testing processes, where the optimization explicitly promotes a balanced classification accuracy between seen and unseen classes.
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
Ranked #5 on
Generalized Zero-Shot Learning
on SUN Attribute
