© 2018 Curran Associates Inc.All rights reserved. It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. Thus, by estimating this matrix, classifiers can escape from overfitting those noisy labels. However, such estimation is practically difficult, due to either the indirect nature of two-step approaches, or not big enough data to afford end-to-end approaches. In this paper, we propose a human-assisted approach called “Masking” that conveys human cognition of invalid class transitions and naturally speculates the structure of the noise transition matrix. To this end, we d...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small...
Learning with noisy labels is imperative in the Big Data era since it reduces expensive labor on acc...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
Machine learning is an ever-growing and increasingly pervasive presence in every-day life; we entrus...
Despite being robust to small amounts of label noise, convolutional neural networks trained with sto...
The learning with noisy labels has been addressed with both discriminative and generative models. Al...
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pi...
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings ...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small...
Learning with noisy labels is imperative in the Big Data era since it reduces expensive labor on acc...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
Machine learning is an ever-growing and increasingly pervasive presence in every-day life; we entrus...
Despite being robust to small amounts of label noise, convolutional neural networks trained with sto...
The learning with noisy labels has been addressed with both discriminative and generative models. Al...
Noisy labels damage the performance of deep networks. For robust learning, a prominent two-stage pi...
The final publication is available at Springer via http://dx.doi.org/10.1007/11499305_60Proceedings ...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...