Abstract. In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, ...

In this paper, we theoretically study the problem of binary classification in the presence of random classification noise --- the learner, instead of seeing the ...

A curated list of resources for Learning with Noisy Labels - subeeshvasu/Awesome-Learning-with-Label-Noise.

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.

Aug 29, 2022 · I work in computer vision but I'd really be interested to read about such results in any domain of deep learning.

The idea is that when training with noisy labels, the model starts by learning the patterns in data samples with correct labels. Later in training, the model ...

A curated list of most recent papers & codes in Learning with Noisy Labels. Some recent works about group-distributional robustness, label distribution shifts, ...

As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels(robust training) is becoming an important ...

This paper stud- ies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NETAB (as.

Feb 23, 2024 · This book defines the different types of label noise, introduces the theory behind the problem, presents the main techniques that enable the ...