Uncertainty-aware Sampling for Long-tailed Semi-supervised Learning
Kuo Yang Duo Li Menghan Hu Guangtao Zhai Xiaokang Yang Xiao-Ping Zhang
For semi-supervised learning with imbalance classes, the long-tailed
distribution of data will increase the model prediction bias toward dominant
classes, undermining performance on less frequent classes. Existing methods
also face challenges in ensuring the selection of sufficiently reliable
pseudo-labels for model training and there is a lack of mechanisms to adjust
the selection of more reliable pseudo-labels based on different training
stages. To mitigate this issue, we introduce uncertainty into the modeling
process for pseudo-label sampling, taking into account that the model
performance on the tailed classes varies over different training stages. For
example, at the early stage of model training, the limited predictive accuracy
of model results in a higher rate of uncertain pseudo-labels. To counter this,
we propose an Uncertainty-Aware Dynamic Threshold Selection (UDTS) approach.
This approach allows the model to perceive the uncertainty of pseudo-labels at
different training stages, thereby adaptively adjusting the selection
thresholds for different classes. Compared to other methods such as the
baseline method FixMatch, UDTS achieves an increase in accuracy of at least
approximately 5.26%, 1.75%, 9.96%, and 1.28% on the natural scene image
datasets CIFAR10-LT, CIFAR100-LT, STL-10-LT, and the medical image dataset
TissueMNIST, respectively. The source code of UDTS is publicly available at:
https://github.com/yangk/UDTS.