# Training deep neural-networks using a noise adaptation layer

@inproceedings{Goldberger2016TrainingDN, title={Training deep neural-networks using a noise adaptation layer}, author={Jacob Goldberger and Ehud Ben-Reuven}, booktitle={International Conference on Learning Representations}, year={2016} }

The availability of large datsets has enabled neural networks to achieve impressive recognition results. [] Key Method Thus we can apply the EM algorithm to find the parameters of both the network and the noise and to estimate the correct label. In this study we present a neural-network approach that optimizes the same likelihood function as optimized by the EM algorithm. The noise is explicitly modeled by an additional softmax layer that connects the correct labels to the noisy ones.

## 488 Citations

### Deep Neural Networks for Corrupted Labels

- Computer ScienceDeep Learning: Concepts and Architectures
- 2019

An approach for learning deep networks from datasets corrupted by unknown label noise is described, which append a nonlinear noise model to a standard deep network, which is learned in tandem with the parameters of the network.

### Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

- Computer ScienceICML
- 2019

This paper finds that the test accuracy can be quantitatively characterized in terms of the noise ratio in datasets, and adopts the Co-teaching strategy which takes full advantage of the identified samples to train DNNs robustly against noisy labels.

### Learning from Noisy Labels with Noise Modeling Network

- Computer ScienceArXiv
- 2020

The state-of-the-art of training classifiers are extended by modeling noisy and missing labels in multi-label images with a new Noise Modeling Network (NMN) that follows the authors' convolutional neural network (CNN) and integrates with it, forming an end-to-end deep learning system, which can jointly learn the noise distribution and CNN parameters.

### DAT: Training Deep Networks Robust to Label-Noise by Matching the Feature Distributions

- Computer Science2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2021

The DAT method is proposed, which is the first to address the noisy label problem from the perspective of the feature distribution, and can consistently outperform other state-of-the-art methods.

### The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels

- Computer ScienceArXiv
- 2022

A new empirical result is reported: for each example, when looking at the time it has been memorized by each model in an ensemble of networks, the diversity seen in noisy examples is much larger than the clean examples.

### Learning to Learn From Noisy Labeled Data

- Computer Science2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019

This work proposes a noise-tolerant training algorithm, where a meta-learning update is performed prior to conventional gradient update, and trains the model such that after one gradient update using each set of synthetic noisy labels, the model does not overfit to the specific noise.

### A Spectral Perspective of DNN Robustness to Label Noise

- Computer ScienceAISTATS
- 2022

This work relates the smoothness regularization that usually exists in conventional training to the attenuation of high frequencies, which mainly character-ize noise, and suggests that one may further improve robustness via spectral normalization.

### Training Robust Deep Neural Networks on Noisy Labels Using Adaptive Sample Selection with Disagreement

- Computer ScienceIEEE Access
- 2021

An adaptive sample selection method to train deep neural networks robustly and prevent noise contamination in the disagreement strategy is proposed and improves generalization performance in an image classification task with simulated noise rates of up to 50%.

### JoSDW: Combating Noisy Labels by Dynamic Weight

- Computer ScienceFuture Internet
- 2022

A small loss sample selection strategy with dynamic weight is designed that increases the proportion of agreement based on network predictions, gradually reduces the weight of the complex sample, and increases the Weight of the pure sample at the same time.

### Noisy Labels Can Induce Good Representations

- Computer ScienceArXiv
- 2020

It is observed that if an architecture â€śsuitsâ€ť the task, training with noisy labels can induce useful hidden representations, even when the model generalizes poorly; i.e., the last few layers of the model are more negatively affected by noisy labels.

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