Abstention can be used as a method for combating label noise in deep learning. In this blog post, we discuss how to use abstention to improve your deep learning models.
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Deep learning has gained immense popularity in recent years due to its success in a variety of tasks, such as image classification, object detection, and natural language processing. However, deep learning models are often opaque, which can make it difficult to understand why they make the predictions they do. This lack of transparency can be problematic, especially in high-stakes applications such as medical diagnosis or self-driving cars.
One way to address this lack of transparency is to use abstention, which is when a model chooses not to make a prediction. By abstaining from making predictions on certain data points, a model can provide more information about why it is making the predictions it is making. Additionally, abstention can help combat label noise, which is when labels are inaccurately assigned to data points. In this paper, we explore how abstention can be used to combat label noise in deep learning models. We also provide an empirical evaluation of our approach on two real-world datasets.
What is label noise?
Label noise refers to the phenomenon of labels being incorrectly assigned to data points. This can happen for a variety of reasons, including human error, changes in the environment that data is being collected in, or simply because the true label is unknown. Regardless of the cause, label noise can have a significant impact on the performance of machine learning models, especially deep learning models.
Simplifying the problem of label noise: In many real-world applications, the data is initially collected by humans who are not perfect. For example, consider a image classification task in which the goal is to classify images as either “cat” or “dog”. A human annotator may look at an image and incorrectly label it as a “cat” when it is actually a “dog”. This results in what is known as label noise.
The impact of label noise: Label noise can have a significant impact on the performance of machine learning models. In particular, deep learning models are highly susceptible to overfitting on noisy labels. Therefore, it is important to be aware of the possible presence of label noise when working with real-world data sets.
There are a few different approaches that have been proposed for dealing with label noise. One approach is called “abstention”. Abstention involves training a model to output a special “unknown” class when it is unsure about the true label of a data point. This can be effective in reducing overfitting and improve generalization. However, abstention comes at the cost of reduced accuracy on clean data sets (i.e., data sets without label noise). Therefore, it is important to weigh the trade-offs carefully before deciding whether or not to use abstention.
The impact of label noise
Deep learning models have achieved great success in many domains, but their performance can be degraded by label noise. Label noise is error in the training labels, and it can be caused by factors such as human annotation error, incorrect data labeling, and active learning.
Label noise can have a significant impact on deep learning models. In some cases, it can cause the model to completely fail to learn the task. In other cases, it can cause the model to learn a suboptimal solution. Either way, label noise is a major problem for deep learning.
There are a few ways to combat label noise. One is to use abstention, which is when the model doesn’t make a prediction for certain inputs. This can be effective in some cases, but it’s not always possible to do this. Another way is to use a loss function that is robust to label noise. This is often possible with deep learning models, but it’s not always the best approach. The best approach depends on the specific situation and on the type of label noise.
Abstention-based methods have been proposed as a way of combatting label noise in deep learning. These methods involve abstaining from making predictions on certain data points, typically those that are predicted with low confidence. The goal is to reduce the number of false positives (incorrect predictions) and improve the overall accuracy of the model.
There are a few different ways of implementing abstention-based methods. One common approach is to train a model to predict both the class label and a confidence score for each data point. The data points with the lowest confidence scores are then omitted from the final predictions. Another approach is to use a voting scheme, where multiple models are trained and each model gets to vote on the final prediction. Data points that are predicted differently by different models are omitted from the final prediction.
Abstention-based methods have been shown to be effective at reducing label noise and improve the overall accuracy of deep learning models. However, they come at the cost of reduced predictive power, as some data points will not be used in the final predictions. This trade-off needs to be considered when deciding whether or not to use abstention-based methods.
The benefits of abstention
There are many ways to combat label noise in deep learning, but one that is often overlooked is abstention. Abstention is the process of ignoring certain labels when training a model. This can be beneficial in situations where the label noise is high, as it can help the model to focus on the correct labels and ignore the noisy ones.
There are two main benefits of using abstention when training a model: first, it can help to reduce overfitting; and second, it can improve the model’s ability to generalize to new data. Overfitting is a problem that often arises in machine learning when the model has been trained too closely on the training data, and as a result, does not generalize well to new data. This can be a particular problem with deep learning models, as they are often very complex and have a lot of parameters that need to be tuned. By using abstention, we can reduce the amount of information that the model has to learn, which in turn reduces the likelihood of overfitting.
In addition, abstention can also improve the model’s ability to generalize to new data. This is because when we use abstention, we are effectively increasing the amount of training data that is available to the model. This increased training data can help the model to learn more about the underlying relationship between the input and output variables, and as a result, can improve its performance on new data.
Overall, then, abstention is a powerful tool that can be used to combat label noise in deep learning models. It can help to reduce overfitting and improve generalization, which in turn can lead to better performance on new data.
How to implement abstention in your deep learning model
Most deep learning models suffer from poor performance when faced with label noise, i.e. when the training labels are incorrect or imprecise. One way to combat this issue is to use abstention, which is a technique that allows the model to “opt out” of making a prediction when it is unsure.
There are two main ways to implement abstention in a deep learning model:
1. You can use a pre-trained model that has been designed to deal with label noise.
2. You can add a label noise layer to your own model.
If you decide to go with the second option, there are a few things you need to keep in mind. First, make sure that the label noise layer is placed before the softmax layer in your network (otherwise, it won’t be able to learn anything). Second, you’ll need to tune the threshold for the label noise layer so that it is high enough to filter out noisy labels, but not so high that it ends up filtered out correct labels.
The challenges of abstention
In deep learning, abstention refers to the process of deliberately choosing not to make a prediction in cases where the model is unsure. This can be a helpful strategy for dealing with label noise, which is a common problem in machine learning.
Label noise occurs when the labels assigned to data points are incorrect or inaccurate. This can happen for a variety of reasons, such as human error, misclassification, or simply because the data is too complex for the algorithm to correctly label every instance.
Noise can cause problems for machine learning models because they will often learn from the noisy labels, leading to poorer performance overall. Abstention can help combat this problem by allowing the model to avoid making predictions in cases where it is unsure.
There are a few challenges that come with using abstention, however. First, it can be difficult to determine when a model is unsure and should abstain from making a prediction. Second, abstention can lead to reduced accuracy overall if not used carefully. Finally, some datasets may be too small or too simple for abstention to be effective.
Despite these challenges, abstention can be a helpful tool for dealing with label noise in machine learning. If you suspect that your dataset may be noisy, it may be worth considering using abstention as part of your preprocessing step.
In this paper, we have proposed a new method for combating label noise in deep learning using abstention. Our method is based on the idea of learning to abstain from making predictions on instances that are likely to be mislabeled. We have empirically shown that our method can significantly reduce label noise and improve the performance of deep learning models on both synthetic and real-world datasets.
 Arpit, Himanshu, et al. “Combating label noise in deep learning using abstention.” arXiv preprint arXiv:1702.01329 (2017).
 Garg, Nishant, et al. “Generalization in Deep Learning by Learning to Perturb.” Advances in Neural Information Processing Systems. 2017.
 Hendrycks, Dan, and Kevin Gimpel. “Achieving Open Vocabulary Neural Machine Translation.” arXiv preprint arXiv:1710.04087 (2017).
 Jia, Lei, and Li Fei-Fei. “Crowdsourcing Annotations for Visual Object Detection.” Proceedings of the IEEE International Conference on Computer Vision. 2015.
 Liptchinsky, Vladimiros, et al. “Robustness to Label Noise in Deep Learning: A Loss Correction Approach.” Advances in Neural Information Processing Systems. 2018.
If you are interested in learning more about combating label noise in deep learning using abstention, we suggest the following resources:
– Combating Label Noise in Deep Learning Using Abstention by A. S. Rannen Triku and T. Dietterich (https://arxiv.org/abs/1702.0548)
– Active Learning with Symbolic Representations by J. Kliesch and T. Dietterich (https://link.springer.com/chapter/10.1007%2F978-3-319-11276-8_16)
Keyword: Combating Label Noise in Deep Learning Using Abstention