Add to ORKGAbstract—Label noise is not uncommon in machine learning applications nowadays and imposes great challenges for many existing classifiers. In this paper we propose a new type of auto-encoder coined label-denoising auto-encoder to learn a representation for robust classification under this situation. For this purpose, we include both the feature and the (noisy) label of a data point in the input layer of the auto-encoder network, and during each learning iteration, we disturb the label according to the posterior probability of the data estimated by a softmax regression classifier. The learnt representation is shown to be robust against label noise on three real-world data-sets. I
In many real-world classification problems, the labels of training examples are randomly corrupted. ...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
In classification, it is often difficult or expensive to obtain completely accurate and reliable lab...
In real applications, label noise and feature noise are two main noise sources. Similar to feature n...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Label noise is an important issue in classification, with many potential negative consequences. For ...
We present a representation learning method that learns features at multiple dif-ferent levels of sc...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
We present a representation learning method that learns features at multiple dif-ferent levels of sc...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
In many applications of classifier learning, training data suffers from label noise. Deep networks a...
Label noise is prevalent in real-world visual learning applications and correcting all label mistake...
In many real-world classification problems, the labels of training examples are randomly corrupted. ...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
In classification, it is often difficult or expensive to obtain completely accurate and reliable lab...
In real applications, label noise and feature noise are two main noise sources. Similar to feature n...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Label noise is an important issue in classification, with many potential negative consequences. For ...
We present a representation learning method that learns features at multiple dif-ferent levels of sc...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
We present a representation learning method that learns features at multiple dif-ferent levels of sc...
Large scale datasets collected using non-expert labelers are prone to labeling errors. Errors in the...
In many applications of classifier learning, training data suffers from label noise. Deep networks a...
Label noise is prevalent in real-world visual learning applications and correcting all label mistake...
In many real-world classification problems, the labels of training examples are randomly corrupted. ...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
In classification, it is often difficult or expensive to obtain completely accurate and reliable lab...