Summary of Stochastic Resetting Mitigates Latent Gradient Bias Of Sgd From Label Noise, by Youngkyoung Bae et al.
Stochastic Resetting Mitigates Latent Gradient Bias of SGD from Label Noise
by Youngkyoung Bae, Yeongwoo Song, Hawoong Jeong
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 A new study reveals that restarting deep neural network (DNN) training can significantly improve generalization performance when dealing with noisy labels. The researchers found that DNNs initially learn patterns in the data but then overfit to corrupted data, leading to poor performance. By analyzing stochastic gradient descent (SGD), they identified a latent gradient bias caused by noisy labels, which hinders generalization. To address this issue, they developed a stochastic resetting method inspired by statistical physics techniques for efficient target searches. Theoretical analysis and empirical validation confirm the benefits of resetting, with significant improvements achieved. The approach is easy to implement and compatible with other methods for handling noisy labels. Furthermore, the study provides insights into DNN learning dynamics from an interpretability perspective, expanding understanding of training methods through a statistical physics lens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When training deep neural networks (DNNs) with noisy labels, it’s like searching for a target in a maze – sometimes you need to start over! A new research shows that resetting DNN training can greatly improve how well the networks perform. This happens because initially, they learn patterns in the data, but then they get stuck on bad information and forget the good stuff. By looking at how DNNs train using something called stochastic gradient descent (SGD), researchers found a hidden problem caused by noisy labels that makes generalization worse. To fix this, they created a new method inspired by ways to quickly find targets in statistical physics. The study shows that this approach works well and is easy to use with other methods for handling noisy labels. It also helps us understand how DNNs learn, which can help us make better AI systems. |
Keywords
» Artificial intelligence » Generalization » Neural network » Stochastic gradient descent
