Summary of Making Sigmoid-mse Great Again: Output Reset Challenges Softmax Cross-entropy in Neural Network Classification, by Kanishka Tyagi et al.
Making Sigmoid-MSE Great Again: Output Reset Challenges Softmax Cross-Entropy in Neural Network Classification
by Kanishka Tyagi, Chinmay Rane, Ketaki Vaidya, Jeshwanth Challgundla, Soumitro Swapan Auddy, Michael Manry
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The study investigates the effectiveness of two objective functions, Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE), in neural network classification tasks. The researchers introduce the Output Reset algorithm to reduce inconsistent errors and enhance classifier robustness. They demonstrate that MSE with sigmoid activation achieves comparable accuracy and convergence rates to SCE on benchmark datasets such as MNIST, CIFAR-10, and Fashion-MNIST. Furthermore, they show that MSE outperforms SCE in scenarios with noisy data, challenging conventional wisdom about neural network training strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at two ways to train neural networks for classification: Mean Squared Error (MSE) and Softmax Cross-Entropy (SCE). The researchers try a new method called Output Reset that helps reduce mistakes. They test these methods on famous datasets like MNIST, CIFAR-10, and Fashion-MNIST. Surprisingly, they find that MSE is just as good as SCE in many cases, especially when the data is noisy. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Mse » Neural network » Sigmoid » Softmax
