Add to ORKGMachine learning is an ever-growing and increasingly pervasive presence in every-day life; we entrust these models, and systems built on these models, with some of our most sensitive information and security applications. However, for all of the trust that we place in these models, it is essential to recognize the fact that such models are simply reflections of the data and labels on which they are trained. To wit, if the data and labels are suspect, then so too must be the models that we rely on—yet, as larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information. While recent work has begun to investigate mitigating the eff...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can ea...
Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
The drastic increase of data quantity often brings the severe decrease of data quality, such as inco...
Supervised learning has seen numerous theoretical and practical advances over the last few decades. ...
Modern machine learning techniques have demonstrated their excellent capabilities in many areas. Des...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obta...
This thesis addresses three challenge of machine learning: high-dimensional data, label noise and li...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can ea...
Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as...
This thesis focuses on the aspect of label noise for real-life datasets. Due to the upcoming growing...
Labelling of data for supervised learning canbe costly and time-consuming and the riskof incorporati...
Over the past decades, deep neural networks have achieved unprecedented success in image classificat...
The drastic increase of data quantity often brings the severe decrease of data quality, such as inco...
Supervised learning has seen numerous theoretical and practical advances over the last few decades. ...
Modern machine learning techniques have demonstrated their excellent capabilities in many areas. Des...
Recent advances in Artificial Intelligence (AI) have been built on large scale datasets. These advan...
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier ca...
Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obta...
This thesis addresses three challenge of machine learning: high-dimensional data, label noise and li...
Label noise in real-world datasets encodes wrong correlation patterns and impairs the generalization...
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can ea...
Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high...