Rethinking Performance Estimation in Neural Arc
Rethinking Performance Estimation in Neural Architecture Search 1、簡介 **解決的問題：**以往訓練一個性能估計器，網絡需要大量的計算資源（需要訓練大量的網絡）。爲了高效的估計網絡的性能，提出一種MIP方法（Minimum Importance Pruning）。 另外，以前的方法無法證明訓練的子網絡結構和從頭訓練的網絡性能具有
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相關文章
- 1. Rethinking on Multi-Stage Networks for Human Pose Estimation
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- 3. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes—2017（筆記）
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- 5. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 6. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 8. Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
- 9. Understanding deep learning requires rethinking generalization
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