Summary of Network Fission Ensembles For Low-cost Self-ensembles, by Hojung Lee et al.
Network Fission Ensembles for Low-Cost Self-Ensembles
by Hojung Lee, Jong-Seok Lee
First submitted to arxiv on: 5 Aug 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 This paper proposes a novel approach to ensemble learning for image classification, called Network Fission Ensembles (NFE). The method converts a conventional network into a multi-exit structure, enabling multiple outputs from a single network without requiring additional models. NFE starts by pruning weights to reduce training burden, then groups remaining weights into sets and creates auxiliary paths to construct exits. This process, dubbed Network Fission, allows for ensemble learning with no extra computational cost. The paper shows that the multi-exits improve performance via regularization, even when the network is sparse. The proposed method achieves significant improvement compared to existing ensemble methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to make image classification better by combining many models into one. It’s like taking multiple photos and using them to create a single, really good photo. This new approach, called Network Fission Ensembles (NFE), makes it easier to combine lots of smaller models into one big model that works really well. The best part is that it doesn’t need any extra computers or processing power. The method starts by making some parts of the original model smaller, then adds more paths to help the model make better decisions. This helps the model learn from its mistakes and become even better. As a result, the new approach can make images more accurate than before. |
Keywords
» Artificial intelligence » Image classification » Pruning » Regularization
