Summary of Consistentfeature: a Plug-and-play Component For Neural Network Regularization, by Ruizhe Jiang et al.
ConsistentFeature: A Plug-and-Play Component for Neural Network Regularization
by RuiZhe Jiang, Haotian Lei
First submitted to arxiv on: 2 Dec 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 A novel approach is introduced to address the issue of overfitting in neural network models, which often results in significant performance discrepancies between training and test sets. By proposing a simple perspective on overfitting as models learning different representations in different datasets, researchers develop an adaptive method called ConsistentFeature that regularizes feature differences across random subsets of the same training set. This approach is applicable to various architectures and tasks, requiring minimal prior assumptions. The proposed method effectively reduces overfitting, exhibits low sensitivity to hyperparameters, and incurs minimal computational cost. It demonstrates strong memory suppression and promotes normal convergence even when the model has already started to overfit. Additionally, the method consistently improves accuracy and reduces validation loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Overfitting is a problem that makes it hard for neural networks to work well on new data they haven’t seen before. To fix this, scientists have tried many different ways to keep models from getting too good at memorizing their training data. One way to think about overfitting is that models learn to recognize patterns in different datasets. A new method called ConsistentFeature tries to stop this by making sure the model doesn’t get too good at recognizing things it’s already seen before. This works well and can even help if a model has already started to get too good at memorizing its training data. It also makes the model more accurate and better at predicting how well it will do on new data. |
Keywords
» Artificial intelligence » Neural network » Overfitting
