A Noise-Resilient Auto-Labeling Framework With Transition Matrix

Recently, auto-labeling framework has been applied in a lot of applications across various industries. Pseudo-labeling is the most common auto-labeling method and this method is to convert unlabeled data into labeled data by assigning pseudo-labels. Unless we have a perfect model for pseudo-labeling, the additional labeled data we get from unlabeled data always include noisy labels. However, this problem has not been studied by many researchers yet. Addressing this problem, we propose a noise-resilient auto-labeling framework using a transition matrix to mitigate the impact of label noise. The framework consists of three main stages: generating pseudo-labels for unlabeled data, identifying noisy samples based on KL-divergence between estimated transition vectors and model outputs, and using noisy samples as unlabeled data and clean samples as labeled data in semi-supervised learning for training the final model. We also show how much noise is added through pseudo-labeling depending on the initial model’s accuracy. Our experiments demonstrate the proposed method outperforms the state-of-the-art methods for handling noisy labels on both standard classification benchmarks (e.g., CIFAR-10 and CIFAR-100) and real-world datasets (e.g., Clothing100K, Food-101).