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目录
21. AutoSpace: Neural Architecture Search with Less Human Interference(ICCV)
22. CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identifification
23. Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
24. NetAdaptV2: Effificient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
25. NPAS: A Compiler-aware Framework of Unifified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
26. OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
27. Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
28. Prioritized Architecture Sampling with Monto-Carlo Tree Search
29.TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search
30. ViPNAS: Effificient Video Pose Estimation via Neural Architecture Search
31. Compatibility-aware Heterogeneous Visual Search
32. DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
33. Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
34. FP-NAS: Fast Probabilistic Neural Architecture Search
35. Homogeneous Architecture Augmentation for Neural Predictor
36.Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
37. Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization
38. NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
39. NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
40.Neural Architecture Search for Joint Human Parsing and Pose Estimation
41. iNAS: Integral NAS for Device-Aware Salient Object Detection
42.Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search
43.One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
44. RANK-NOSH: Effificient Predictor-Based Architecture Search via Non-Uniform Successive Halving
45.Rethinking Graph Neural Architecture Search from Message-passing
46.Searching by Generating: Flexible and Effificient One-Shot NAS with Architecture Generator
21. AutoSpace: Neural Architecture Search with Less Human Interference(ICCV)
Aim: | 新算法独立自主生成搜索空间,NAS目前仍然需要专家来设计搜索空间。 |
Abstract: | 目前的算法仍然需要专家来设计搜索空间,本文考虑了自动化设计一个搜索空间。这将会面临两个问题: (1)搜索空间爆炸性的复杂度 ( 2)评估不同空间质量的昂贵计算成本 为了解决这些问题,提出可微的适应度评分函数,有效的评估cell的性能和参考架构加速进化过程,避免陷入次优解的方案。 框架通用,兼容而外的计算约束,这使得学习适合不同计算预算的专门搜索空间成为可行的。 |
Conclusion: | 移动模式下(model<500M) ImageNet 精确度 77.8% |
Methods: | 新的可微进化框架、适应度评价函数、额外约束条件 |
Keyresults: | 可微搜索、进化算法 |
Code: | GitHub - zhoudaquan/AutoSpace 只公布了一部分代码 |
22. CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identifification
这篇不像常见的NAS算法。
Aim: | VI-ReID 可见红外线人员重新识别:目的匹配跨模态的行人图像,打破单模态的reID局限性。 |
Abstract: | 现有的人工设计各种双流架构,分别学习特定模态的和模态可共享的表示. 确定适当分离BN层,是促进跨空间性匹配的关键。 新算法由一个面向bn的搜索空间组成,在其中可以通过跨模态任务来完成标准的优化 |
Conclusion: | 在两个基准测试中都优于最先进的方法 改进了SYSU-MM01上的6.70%/6.13%和RegDB上的12.17%/11.23%改进了Rank-1/mAP |
Methods: | Cross-Modality Neural Architecture Search (CM- NAS). |
Keyresults: | SYSU-MM01 、RegDB、Visible-Infrared person re-identifification (VI-ReID) |
Code: | GitHub - JDAI-CV/CM-NAS: CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021) 代码很详细 |
23. Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 无代码 |
24. NetAdaptV2: Effificient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | NetAdapt Project 项目地址在这,只公布了V1,V2没有公布 |
25. NPAS: A Compiler-aware Framework of Unifified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 无代码 |
26. OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | VDIGPKU/OPANAS: The official code for OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection (CVPR 2021) (github) |
27. Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | https://github/Ernie1/Pi-NAS |
28. Prioritized Architecture Sampling with Monto-Carlo Tree Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | GitHub - xiusu/NAS-Bench-Macro: NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021 |
29.TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | Index of /dataset/TransNAS-Bench-101/ 相关项目地址 Vega |
30. ViPNAS: Effificient Video Pose Estimation via Neural Architecture Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 论文中有一些相关代码,但是并没有给全。 |
31. Compatibility-aware Heterogeneous Visual Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
32. DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
33. Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
34. FP-NAS: Fast Probabilistic Neural Architecture Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
35. Homogeneous Architecture Augmentation for Neural Predictor(ICCV)
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | GitHub - lyq998/HAAP: Homogeneous Architecture Augmentation for Neural Predictor |
36.Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | GitHub - kcyu2014/nas-landmarkreg: [CVPR2021] Code for Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search |
37. Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
38. NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 提供训练模型 https://github/NAS-OA/NASOA |
39. NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
40.Neural Architecture Search for Joint Human Parsing and Pose Estimation
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | https://github/GuHuangAI/NPP |
41. iNAS: Integral NAS for Device-Aware Salient Object Detection
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 主页iNAS: Integral NAS for Device-Aware Salient Object Detection – 程明明个人主页 代码GitHub - guyuchao/iNAS: Open Source Neural Architecture Search Toolbox for Device-aware Image Dense Prediction & Official implementation of ICCV2021 "iNAS: Integral NAS for Device-Aware Salient Object Detection" |
42.Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没代码 |
43.One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | https://github/researchmm/NEAS |
44. RANK-NOSH: Effificient Predictor-Based Architecture Search via Non-Uniform Successive Halving
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | 没开源 |
45.Rethinking Graph Neural Architecture Search from Message-passing
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | https://github/phython96/GNAS-MP |
46.Searching by Generating: Flexible and Effificient One-Shot NAS with Architecture Generator
Aim: | |
Abstract: | |
Conclusion: | |
Methods: | |
Keyresults: | |
Code: | https://github/eric8607242/SGNAS |
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