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Found 3 papers, 2 papers with code
Latent-based Diffusion Model for Long-tailed Recognition
We propose a new approach, the Latent-based Diffusion Model for Long-tailed Recognition (LDMLR), as a feature augmentation method to tackle the issue.
Model Calibration in Dense Classification with Adaptive Label Perturbation
To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image.
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning
Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process.
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