The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Learning From Noisy Labels With Deep Neural Networks: A Survey
Open accessAbstract
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 62 state-of-the-art robust training methods, all of which are categorized into five groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we perform an in-depth analysis of noise rate estimation and summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies.
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Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Burns, C., Izmailov, P., Kirchner, J. H., Baker, B., Gao, L., Aschenbrenner, L., Chen, Y., Ecoffet, A., Joglekar, M., Leike, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.09390 · Cited by 34
Metadata
- Authors
- Song, H., Kim, M., Park, D., Shin, Y., & Lee, J.-G.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2023-11
- Volume
- 34
- Issue
- 11
- Pages
- 8135-8153
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3042609801
- Cited by
- 1,308
- References
- 154
- FWCI
- 78.92
- Type
- journal article
- Language
- en
- Open access
- green
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