<ul><li>The performance of deep neural networks scales with dataset size and label quality.</li><li>In this work, a Weighted Adaptive Nearest Neighbor (WANN) approach is proposed to mitigate low-quality data annotations.</li><li>WANN outperforms reference methods and exhibits superior generalization on imbalanced data.</li><li>The proposed weighting scheme enhances supervised dimensionality reduction and minimizes latency and storage requirements.</li></ul>

An Embedding is Worth a Thousand Noisy Labels

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