Regularized Evolution for Image Classifier Architecture Search--閱讀筆記
論文來源：AAAI 2019 論文鏈接：Regularized Evolution for Image Classifier Architecture Search Abstract 這篇論文開發了一種圖像分類器AmoebaNet-A，它首次超越了手工設計。爲此，我們通過引入年齡屬性來修改比賽選擇進化算法，以支持年輕的基因型。 當AmoebaNet-A具有與人工設計的模型大小一致時，二者精度相當；
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相關文章
- 1. automl-演化算法-Regularized evolution for image classifier architecture search-論文筆記
- 2. 【論文閱讀筆記】Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
- 3. 《CARS: Continuous Evolution for Efficient Neural Architecture Search》閱讀記錄
- 4. 【閱讀筆記】Adversarially Regularized Autoencoders
- 5. 【閱讀筆記】《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》
- 6. NASE Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search 閱讀筆記
- 7. AutoGAN: Neural Architecture Search for Generative Adversarial Networks閱讀筆記
- 8. 論文閱讀筆記：《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》
- 9. Deep Residual Learning for Image Recognition閱讀筆記
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