Rethinking Performance Estimation in Neural Arc

时间 2021-01-14

标签论文笔记深度学习繁體版

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Rethinking Performance Estimation in Neural Architecture Search 1、简介 **解决的问题：**以往训练一个性能估计器，网络需要大量的计算资源（需要训练大量的网络）。为了高效的估计网络的性能，提出一种MIP方法（Minimum Importance Pruning）。另外，以前的方法无法证明训练的子网络结构和从头训练的网络性能具有

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1. Rethinking on Multi-Stage Networks for Human Pose Estimation
2. Synthesizer: Rethinking Self-Attention in Transformer Models
3. PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes—2017（笔记）
4. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
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
10. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

>>更多相关文章<<