In supervised learning, the question of data quality and curation has been overshadowed in recent years by increasingly more powerful and expressive models that can ingest internet-scale data.

This prevailing paradigm heavily relies on the out-of-distribution (OOD) effect to influence the model's prediction.

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Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

We start from an intuition that many complex visual data, e.g., videos and 3D scenes, are represented