Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis. (arXiv:2109.07509v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2109.07509

Visual sentiment analysis has received increasing attention in recent years.
However, the dataset's quality is a concern because the sentiment labels are
crowd-sourcing, subjective, and prone to mistakes, and poses a severe threat to
the data-driven models, especially the deep neural networks. The deep models
would generalize poorly on the testing cases when trained to over-fit the
training samples with noisy sentiment labels. Inspired by the recent progress
on learning with noisy labels, we propose a robust learning method to …

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