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Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation
Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alternative by capturing the joint mechanism that produces features, clean labels, and corrupted observations. However, prior work typically (i) introduces
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