From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

• URL: http://arxiv.org/abs/2205.00690v1
• Date: Mon, 2 May 2022 07:15:45 GMT
• Title: From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
• Authors: HeeSun Bae, Seungjae Shin, JoonHo Jang, Byeonghu Na, Kyungwoo Song and Il-Chul Moon
• Abstract summary: Noisy Prediction (NPC) is a new approach to learning with noisy labels. NPC corrects the noisy prediction from the pre-trained classifier to the true label as a post-processing scheme. Our method boosts the classification performances of all baseline models on both synthetic and real-world datasets.
• Score: 22.722830935155223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization power of a classifier by making the classifier be trained to be overfitted to wrong labels. Existing methods on noisy label have focused on modifying classifier training procedure. It results in two possible problems. First, these methods are not applicable to a pre-trained classifier without further access into training. Second, it is not easy to train a classifier and remove all of negative effects from noisy labels simultaneously. From these problems, we suggests a new branch of approach, Noisy Prediction Calibration (NPC) in learning with noisy labels. Through the introduction and estimation of a new type of transition matrix via generative model, NPC corrects the noisy prediction from the pre-trained classifier to the true label as a post-processing scheme. We prove that NPC theoretically aligns with the transition matrix based methods. Yet, NPC provides more accurate pathway to estimate true label, even without involvement in classifier learning. Also, NPC is applicable to any classifier trained with noisy label methods, if training instances and its predictions are available. Our method, NPC, boosts the classification performances of all baseline models on both synthetic and real-world datasets.

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