  Deep neural networks have proven to be highly effective when large amounts of
data with clean labels are available. However, their performance degrades when
training data contains noisy labels, leading to poor generalization on the test
set. Real-world datasets contain noisy label samples that either have similar
visual semantics to other classes (in-distribution) or have no semantic
relevance to any class (out-of-distribution) in the dataset. Most
state-of-the-art methods leverage ID labeled noisy samples as unlabeled data
for semi-supervised learning, but OOD labeled noisy samples cannot be used in
this way because they do not belong to any class within the dataset. Hence, in
this paper, we propose incorporating the information from all the training data
by leveraging the benefits of self-supervised training. Our method aims to
extract a meaningful and generalizable embedding space for each sample
regardless of its label. Then, we employ a simple yet effective K-nearest
neighbor method to remove portions of out-of-distribution samples. By
discarding these samples, we propose an iterative "Manifold DivideMix"
algorithm to find clean and noisy samples, and train our model in a
semi-supervised way. In addition, we propose "MixEMatch", a new algorithm for
the semi-supervised step that involves mixup augmentation at the input and
final hidden representations of the model. This will extract better
representations by interpolating both in the input and manifold spaces.
Extensive experiments on multiple synthetic-noise image benchmarks and
real-world web-crawled datasets demonstrate the effectiveness of our proposed
framework. Code is available at https://github.com/Fahim-F/ManifoldDivideMix.
