A curated list of neural architecture search and related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search.
Please feel free to pull requests or open an issue to add papers.
Type | G | RL | EA | PD | Other |
---|---|---|---|---|---|
Explanation | gradient-based | reinforcement learning | evaluationary algorithm | performance prediction | other types |
Title | Venue | Type | Code |
---|---|---|---|
Neural Architecture Search with Reinforcement Learning | ICLR | RL | - |
Designing Neural Network Architectures using Reinforcement Learning | ICLR | RL | - |
Neural Optimizer Search with Reinforcement Learning | ICML | RL | - |
Learning Curve Prediction with Bayesian Neural Networks | ICLR | PD | - |
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization | ICLR | PD | - |
Hyperparameter Optimization: A Spectral Approach | NeurIPS-W | Other | github |
Title | Venue | Type | Code |
---|---|---|---|
Speeding up Automatic Hyperparameter Optimization of Deep Neural Networksby Extrapolation of Learning Curves | IJCAI | PD | github |
- What’s the deal with Neural Architecture Search?
- Google Could AutoML
- PocketFlow
- AutoML Challenge
- AutoDL Challenge
- MnasNet: Platform-Aware Neural Architecture Search for Mobile [pdf] [code]
- Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le. arXiv 2018.07
- Population Based Training of Neural Networks [pdf]
- Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu. arXiv 1711
- NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search [pdf]
- Lu, Zhichao and Whalen, Ian and Boddeti, Vishnu and Dhebar, Yashesh and Deb, Kalyanmoy and Goodman, Erik and Banzhaf, Wolfgang, arXiv 1810
- Random Search and Reproducibility for Neural Architecture Search
- Liam Li, Ameet Talwalkar. arXiv 1901
- Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells [pdf]
- Nekrasov, Vladimir and Chen, Hao and Shen, Chunhua and Reid, Ian. arXiv 1810
- Training Frankenstein’s Creature to Stack: HyperTree Architecture Search [pdf]
- Andrew Hundt, Varun Jain, Chris Paxton, Gregory D. Hager. arXiv 1810
- Searching for efficient multi-scale architectures for dense image prediction [pdf]
- Chen, Liang-Chieh and Collins, Maxwell D and Zhu, Yukun and Papandreou, George and Zoph, Barret and Schroff, Florian and Adam, Hartwig and Shlens, Jonathon. NeurIPS 2018
- Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation [pdf]
- arXiv 1901
- Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search [pdf] [code]
- Neural Architecture Search: A Survey
- Thomas Elsken, Jan Hendrik Metzen, Frank Hutter. arXiv 1808
- Taking Human out of Learning Applications: A Survey on Automated Machine Learning [pdf]
- Yao Quanming, Wang Mengshuo, Jair Escalante Hugo, Guyon Isabelle, Hu Yi-Qi, Li Yu-Feng, Tu Wei-Wei, Yang Qiang, Yu Yang. arXiv 1810
Architecture | Top-1 (%) | Top-5 (%) | Params (M) | +x (M) | GPU | Days |
---|---|---|---|---|---|---|
Inception-v1 | 30.2 | 10.1 | 6.6 | 1448 | - | - |
MobileNet-v1 | 29.4 | 10.5 | 4.2 | 569 | - | - |
ShuffleNet | 26.3 | - | ~5 | 524 | - | - |
NASNet-A | 26.0 | 8.4 | 5.3 | 564 | 450 | 3-4 |
NASNet-B | 27.2 | 8.7 | 5.3 | 488 | 450 | 3-4 |
NASNet-C | 27.5 | 9.0 | 4.9 | 558 | 450 | 3-4 |
AmobebaNet-A | 25.5 | 8.0 | 5.1 | 555 | 450 | 7 |
AmobebaNet-B | 26.0 | 8.5 | 5.3 | 555 | 450 | 7 |
AmobebaNet-C | 24.3 | 7.6 | 6.4 | 555 | 450 | 7 |
Progressive NAS | 25.8 | 8.1 | 5.1 | 588 | 100 | 1.5 |
DARTS-V2 | 26.9 | 9.0 | 4.9 | 595 | 1 | 1 |
GDAS | 26.0 | 8.5 | 5.3 | 581 | 1 | 0.21 |