Summary of Mitigating Instance-dependent Label Noise: Integrating Self-supervised Pretraining with Pseudo-label Refinement, by Gouranga Bala et al.
Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement
by Gouranga Bala, Anuj Gupta, Subrat Kumar Behera, Amit Sethi
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel hybrid framework to mitigate the effects of instance-dependent label noise (IDN) in deep learning models. The framework combines self-supervised learning using SimCLR with iterative pseudo-label refinement. Self-supervised pre-training enables the model to learn robust feature representations without relying on potentially noisy labels, while iterative training refines labels progressively based on confidently predicted samples. Experimental results show that this approach outperforms state-of-the-art methods under high noise conditions, achieving improvements in classification accuracy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps deep learning models work better with noisy data. Noisy data is a big problem because it can make the model learn things that aren’t true. The researchers came up with a new way to fix this by using two techniques together: self-supervised learning and iterative pseudo-label refinement. Self-supervised learning helps the model learn good features without looking at labels, while iterative refinement updates the labels based on how confident the model is about its predictions. This makes the model more accurate and robust when dealing with noisy data. |
Keywords
» Artificial intelligence » Classification » Deep learning » Self supervised
