Summary of An Extensive Study on D2c: Overfitting Remediation in Deep Learning Using a Decentralized Approach, by Md. Saiful Bari Siddiqui et al.
An Extensive Study on D2C: Overfitting Remediation in Deep Learning Using a Decentralized Approach
by Md. Saiful Bari Siddiqui, Md Mohaiminul Islam, Md. Golam Rabiul Alam
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Divide2Conquer (D2C) technique addresses the issue of overfitting in deep learning by partitioning training data into subsets and training identical models independently on each subset. This approach enables the learning of robust patterns while minimizing the influence of outliers and noise. Empirical evaluations demonstrate that D2C enhances generalization performance, particularly with larger datasets, and can be used as a standalone technique or combined with other overfitting reduction methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dive2Conquer is a new way to help deep learning models avoid overfitting by breaking the training data into smaller pieces. It then trains separate copies of the model on each piece, but periodically combines their results. This helps the model learn general patterns while ignoring noisy or outlier data points. By doing so, D2C can make deep learning models more accurate and reliable. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Overfitting
