Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets

• URL: http://arxiv.org/abs/2207.01573v1
• Date: Mon, 4 Jul 2022 16:51:56 GMT
• Title: Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets
• Authors: Paul Albert, Eric Arazo, Noel E. O'Connor and Kevin McGuinness
• Abstract summary: Using search engines for web image retrieval is a tempting alternative to manual curation when creating an image dataset. Their main drawback remains the proportion of incorrect (noisy) samples retrieved. We propose a two stage algorithm starting with a detection step where we use unsupervised contrastive feature learning. We find that the alignment and uniformity principles of contrastive learning allow OOD samples to be linearly separated from ID samples on the unit hypersphere.
• Score: 18.19216557948184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using search engines for web image retrieval is a tempting alternative to manual curation when creating an image dataset, but their main drawback remains the proportion of incorrect (noisy) samples retrieved. These noisy samples have been evidenced by previous works to be a mixture of in-distribution (ID) samples, assigned to the incorrect category but presenting similar visual semantics to other classes in the dataset, and out-of-distribution (OOD) images, which share no semantic correlation with any category from the dataset. The latter are, in practice, the dominant type of noisy images retrieved. To tackle this noise duality, we propose a two stage algorithm starting with a detection step where we use unsupervised contrastive feature learning to represent images in a feature space. We find that the alignment and uniformity principles of contrastive learning allow OOD samples to be linearly separated from ID samples on the unit hypersphere. We then spectrally embed the unsupervised representations using a fixed neighborhood size and apply an outlier sensitive clustering at the class level to detect the clean and OOD clusters as well as ID noisy outliers. We finally train a noise robust neural network that corrects ID noise to the correct category and utilizes OOD samples in a guided contrastive objective, clustering them to improve low-level features. Our algorithm improves the state-of-the-art results on synthetic noise image datasets as well as real-world web-crawled data. Our work is fully reproducible [github].

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