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).

In the semiconductor manufacturing process, Electrical Die Sorting (EDS) is a post-production process used to assess the quality of each chip on the wafer. The results from EDS testing are visualized as a wafer bin map (WBM), which is used for quality control purposes, such as the identification of defective wafers. Recently, deep learning has emerged as a prominent approach for identifying defects in wafers. However, data on defects in the semiconductor industry remain scarce. In this paper, we propose a semi-supervised learning method, TripletMatch, which utilizes triplet loss for unlabeled data. The proposed method extends the FixMatch framework and considers Mixup to smooth decision boundaries. Our experimental results demonstrate the superiority of TripletMatch over various recent deep-learning-based methods and loss functions.