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noisy label project change of paper name

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Dong Yin
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19
noisy_label/README.md
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@@ -1,12 +1,13 @@
<img src="paradigm.png" width="50%">
# A Realistic Simulation Framework for Learning with Label Noise
# An Instance-Dependent Simulation Framework for Learning with Label Noise
We propose a simulation framework for generating realistic instance-dependent
We propose a simulation framework for generating instance-dependent
noisy labels via a pseudo-labeling paradigm. We show that this framework
generates synthetic noisy labels that exhibit important characteristics of the
label noise in practical settings. Equipped with controllable label noise, we
study the negative impact of noisy labels across a few realistic settings to
generates synthetic noisy labels whose distribution is closer to human labels
compared to independent and class-conditional random flipping.
Equipped with controllable label noise, we study the negative impact of
noisy labels across a few practical settings to
understand when label noise is more problematic. Additionally, with the
availability of annotator information from our simulation framework, we propose
a new technique, Label Quality Model (LQM), that leverages annotator features to
@@ -14,7 +15,7 @@ predict and correct against noisy labels. We show that by adding LQM as a label
correction step before applying existing noisy label techniques, we can further
improve the models' performance.
[A Realistic Simulation Framework for Learning with Label Noise](https://arxiv.org/pdf/2107.11413.pdf).
[An Instance-Dependent Simulation Framework for Learning with Label Noise](https://arxiv.org/pdf/2107.11413.pdf).
In this repository, we provide the link to the datasets that we used in Sections
4 and 5 of the above paper, along with a colab that demonstrates how to load the
@@ -68,8 +69,8 @@ The colab example is provided under the Apache License, Version 2.0.
Please use the following bibtex for citations to our paper:
```
@article{gu2021realistic,
title={A Realistic Simulation Framework for Learning with Label Noise},
@article{gu2021instance,
title={An Instance-Dependent Simulation Framework for Learning with Label Noise},
author={Gu, Keren and Masotto, Xander and Bachani, Vandana and Lakshminarayanan, Balaji and Nikodem, Jack and Yin, Dong},
year={2021}
}
@@ -101,7 +102,7 @@ engines such as <a href="https://g.co/datasetsearch">Google Dataset Search</a>.
<td>description</td>
<td><code itemprop="description">
Data accompanying
[A Realistic Simulation Framework for Learning with Label Noise]().
[An Instance-Dependent Simulation Framework for Learning with Label Noise]().
</code></td>
</tr>
<tr>

12
noisy_label/noisy_label_datasets_and_rater_features.ipynb
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@@ -26,15 +26,15 @@
"id": "tAJQfAHhAxz9"
},
"source": [
"# A Realistic Simulation Framework for Learning with Label Noise\n",
"# An Instance-Dependent Simulation Framework for Learning with Label Noise\n",
"\n",
"In this colab, we provide metadata and examples for data loading for the noisy label datasets generated using the pseudo-labeling paradigm propsed in the paper *A Realistic Simulation Framework for Learning with Label Noise*.\n",
"In this colab, we provide metadata and examples for data loading for the noisy label datasets generated using the pseudo-labeling paradigm propsed in the paper *An Instance-Dependent Simulation Framework for Learning with Label Noise*.\n",
"We also provide the associated rater features. We consider 4 tasks: CIFAR10 [1], CIFAR100 [1], Patch Camelyon [2,3], and Cats vs Dogs [4]. For each task, we generate three synthetic noisy label datasets, named as \"low\", \"medium\", and \"high\" according to the amount of label noise.\n",
"\n",
"[1] Krizhevsky, Alex, and Geoffrey Hinton. \"Learning multiple layers of features from tiny images.\", 2009. \\\\\n",
"[2] Veeling, Bastiaan S., Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. \"Rotation equivariant CNNs for digital pathology.\" In International Conference on Medical image computing and computer-assisted intervention, pp. 210-218. Springer, Cham, 2018. \\\\\n",
"[3] Bejnordi, Babak Ehteshami, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak et al. \"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.\" Jama 318, no. 22 (2017): 2199-2210. \\\\\n",
"[4] Elson, Jeremy, John R. Douceur, Jon Howell, and Jared Saul. \"Asirra: a CAPTCHA that exploits interest-aligned manual image categorization.\" In ACM Conference on Computer and Communications Security, vol. 7, pp. 366-374. 2007."
"* [1] Krizhevsky, Alex, and Geoffrey Hinton. \"Learning multiple layers of features from tiny images.\", 2009.\n",
"* [2] Veeling, Bastiaan S., Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. \"Rotation equivariant CNNs for digital pathology.\" In International Conference on Medical image computing and computer-assisted intervention, pp. 210-218. Springer, Cham, 2018.\n",
"* [3] Bejnordi, Babak Ehteshami, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak et al. \"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.\" Jama 318, no. 22 (2017): 2199-2210.\n",
"* [4] Elson, Jeremy, John R. Douceur, Jon Howell, and Jared Saul. \"Asirra: a CAPTCHA that exploits interest-aligned manual image categorization.\" In ACM Conference on Computer and Communications Security, vol. 7, pp. 366-374. 2007."
]
},
{