# Pseudo-label Correction for Instance-dependent Noise Using Teacher-student Framework

arXiv: https://arxiv.org/abs/2311.14237 · HTML: https://mlsanity.com/paper/2311.14237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 12 of 12)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning from Noisy Labels with Distillation](https://mlsanity.com/paper/1703.02391/) (2017)
- [Learning From Noisy Large-Scale Datasets With Minimal Supervision](https://mlsanity.com/paper/1701.01619/) (2017)
- [Learning from Untrusted Data](https://mlsanity.com/paper/1611.02315/) (2016)
- [Combating Label Noise in Deep Learning Using Abstention](https://mlsanity.com/paper/1905.10964/) (2019)
- [Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise](https://mlsanity.com/paper/2012.05458/) (2020)
- [To Smooth or Not? When Label Smoothing Meets Noisy Labels](https://mlsanity.com/paper/2106.04149/) (2021)
- [Classification with Asymmetric Label Noise: Consistency and Maximal Denoising](https://mlsanity.com/paper/1303.1208/) (2013)

## Abstract (arXiv)

The high capacity of deep learning models to learn complex patterns poses a significant challenge when confronted with label noise. The inability to differentiate clean and noisy labels ultimately results in poor generalization. We approach this problem by reassigning the label for each image using a new teacher-student based framework termed P-LC (pseudo-label correction). Traditional teacher-student networks are composed of teacher and student classifiers for knowledge distillation. In our novel approach, we reconfigure the teacher network into a triple encoder, leveraging the triplet loss to establish a pseudo-label correction system. As the student generates pseudo labels for a set of given images, the teacher learns to choose between the initially assigned labels and the pseudo labels. Experiments on MNIST, Fashion-MNIST, and SVHN demonstrate P-LC's superior performance over existing state-of-the-art methods across all noise levels, most notably in high noise. In addition, we introduce a noise level estimation to help assess model performance and inform the need for additional data cleaning procedures.