Add to ORKGLarge-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied annotation quality in image classification tasks, it is still unclear how label noise affects deep learning approaches to image retrieval. In this work, we show that image retrieval methods are less robust to label noise than image classification ones. Furthermore, we, for the first time, investigate different types of label noise specific to image retrieval tasks and study their effect on model performance
Image denoising algorithms have evolved to optimize image quality as measured according to human vis...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
Label errors can have a negative impact on the training of a convolutional neural network for image ...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
High-quality data is necessary for modern machine learning. However, the acquisition of such data is...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
Label noise is a primary point of interest for safety concerns in previous works as it affects the r...
Label noise is omnipresent in the annotations process and has an impact on supervised learning algor...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Noise exists universally in multimedia data, especially in Internet era. For example, tags from web ...
© 1992-2012 IEEE. There is an emerging trend to leverage noisy image datasets in many visual recogni...
In this paper, we introduced the novel concept of advisor network to address the problem of noisy la...
A recurring focus of the deep learning community is to- wards reducing the labeling effort. Data ga...
Label noise is an important issue in classification, with many potential negative consequences. For ...
Image denoising algorithms have evolved to optimize image quality as measured according to human vis...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
Label errors can have a negative impact on the training of a convolutional neural network for image ...
Image classification systems recently made a giant leap with the advancement of deep neural networks...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
High-quality data is necessary for modern machine learning. However, the acquisition of such data is...
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled...
Label noise is a primary point of interest for safety concerns in previous works as it affects the r...
Label noise is omnipresent in the annotations process and has an impact on supervised learning algor...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Noise exists universally in multimedia data, especially in Internet era. For example, tags from web ...
© 1992-2012 IEEE. There is an emerging trend to leverage noisy image datasets in many visual recogni...
In this paper, we introduced the novel concept of advisor network to address the problem of noisy la...
A recurring focus of the deep learning community is to- wards reducing the labeling effort. Data ga...
Label noise is an important issue in classification, with many potential negative consequences. For ...
Image denoising algorithms have evolved to optimize image quality as measured according to human vis...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
Label errors can have a negative impact on the training of a convolutional neural network for image ...