weijiaheng/Advances-in-Label-Noise-Learning
A curated (most recent) list of resources for Learning with Noisy Labels
This resource provides a curated list of research papers and associated code specifically focused on 'Learning with Noisy Labels.' It helps machine learning practitioners and researchers stay current with the latest advancements in training models when the input data has incorrect or unreliable labels. You'll find recent academic work from top conferences, alongside benchmarks and datasets, to inform your model development.
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Use this if you are a machine learning researcher or practitioner who needs to find cutting-edge techniques and datasets for building robust models despite imperfect data labels.
Not ideal if you are looking for an off-the-shelf software tool to automatically clean your data or build a model without deep technical engagement with research papers.
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Oct 18, 2024
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