Summary of Neural Loss Function Evolution For Large-scale Image Classifier Convolutional Neural Networks, by Brandon Morgan and Dean Hougen
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks
by Brandon Morgan, Dean Hougen
First submitted to arxiv on: 30 Jan 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 The proposed paper explores a novel approach to neural network training, seeking alternative loss functions to complement cross-entropy. By minimizing the disparity between learning and evaluation strategies, this research aims to improve classification performance in image classifier convolutional neural networks (CNNs). The authors introduce a new search space for exploring diverse loss functions and employ regularized evolution, a mutation-only genetic algorithm, to identify promising candidates. After eliminating suboptimal options, they demonstrate the effectiveness of three novel loss functions – NeuroLoss1, NeuroLoss2, and NeuroLoss3 – which outperform cross-entropy in multiple experiments across various architectures, datasets, and image augmentation techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to find a better way to teach neural networks. Right now, they’re trained by trying to minimize mistakes (cross-entropy), but their performance is judged based on how accurate they are. This doesn’t make sense, so researchers want to search for alternative loss functions that can be used as easily as cross-entropy. They applied this idea to image classification CNNs and discovered three new loss functions that worked better than the usual one. |
Keywords
* Artificial intelligence * Classification * Cross entropy * Image classification * Neural network
