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Inaccurate Label Distribution Learning with Dependency Noise
In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning, which arise from dependencies on instances and labels.
Building Variable-sized Models via Learngene Pool
To overcome these challenges, motivated by the recently proposed Learngene framework, we propose a novel method called Learngene Pool.
Data Augmentation For Label Enhancement
Existing LE approach have the following problems: (\textbf{i}) They use logical label to train mappings to LD, but the supervision information is too loose, which can lead to inaccurate model prediction; (\textbf{ii}) They ignore feature redundancy and use the collected features directly.
Inaccurate Label Distribution Learning
The previous LDL methods all assumed the LDs of the training instances are accurate.
