Add to ORKGOptimizing neural networks with noisy labels is a challenging task, especially if the label set contains real-world noise. Networks tend to generalize to reasonable patterns in the early training stages and overfit to specific details of noisy samples in the latter ones. We introduce Blind Knowledge Distillation - a novel teacher-student approach for learning with noisy labels by masking the ground truth related teacher output to filter out potentially corrupted knowledge and to estimate the tipping point from generalizing to overfitting. Based on this, we enable the estimation of noise in the training data with Otsus algorithm. With this estimation, we train the network with a modified weighted cross-entropy loss function. We show in our e...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings ...
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades ...
Despite being robust to small amounts of label noise, convolutional neural networks trained with sto...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the ...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pi...
Designing robust loss functions is popular in learning with noisy labels while existing designs did ...
Learning with noisy labels is a vital topic for practical deep learning as models should be robust t...
We propose an algorithm for training neural networks in noisy label scenarios that up-weighs per-exa...
Manually labelling training data for machine learning has always been incredibly time-consuming and ...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings ...
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades ...
Despite being robust to small amounts of label noise, convolutional neural networks trained with sto...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the ...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pi...
Designing robust loss functions is popular in learning with noisy labels while existing designs did ...
Learning with noisy labels is a vital topic for practical deep learning as models should be robust t...
We propose an algorithm for training neural networks in noisy label scenarios that up-weighs per-exa...
Manually labelling training data for machine learning has always been incredibly time-consuming and ...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings ...
Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades ...