Summary of Stochastic Subsampling with Average Pooling, by Bum Jun Kim et al.
Stochastic Subsampling With Average Pooling
by Bum Jun Kim, Sang Woo Kim
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 new module called stochastic average pooling (SAP) to regularize deep neural networks without overfitting. SAP incorporates Dropout-like stochasticity in pooling, eliminating the inconsistency issue that can degrade performance. The authors design the SAP module to achieve regularization without potential degradation and demonstrate consistent improvements across various tasks, datasets, and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix a problem with how we train deep learning models. Right now, a common way to keep our models from getting too good at just memorizing one set of data is called Dropout. But sometimes, this technique can cause issues with the model’s output. The researchers suggest a new approach called stochastic average pooling (SAP) that keeps the benefits of Dropout without the drawbacks. They test their idea and show it really helps improve performance on different tasks and with different types of data. |
Keywords
» Artificial intelligence » Deep learning » Dropout » Overfitting » Regularization
