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

05/02/2022
by   HeeSun Bae, et al.
0

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.

READ FULL TEXT

page 14

page 16

research
02/04/2021

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

Many weakly supervised classification methods employ a noise transition ...
research
05/30/2023

DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling

Learning from noisy labels is a challenge that arises in many real-world...
research
05/31/2023

Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels

Learning from noisy labels is an important and long-standing problem in ...
research
01/31/2019

Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incor...
research
05/24/2018

Cautious Deep Learning

Most classifiers operate by selecting the maximum of an estimate of the ...
research
02/19/2023

Latent Class-Conditional Noise Model

Learning with noisy labels has become imperative in the Big Data era, wh...
research
01/25/2017

Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees

Objective: Using traditional approaches, a Brain-Computer Interface (BCI...

Please sign up or login with your details

Forgot password? Click here to reset