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2020-10-15 22:01:26 +11:00
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CHANGE-LOG.md
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@@ -6,4 +6,4 @@
- [2019.01.31] [13e908f] GDAS codes were publicly released.
- [2020.07.01] [a45808b] Upgrade NAS-API to the 2.0 version.
- [2020.09.16] [7052265] Create NATS-BENCH.
- [2020.10.15] [ ] Update NATS-BENCH to version 1.0
- [2020.10.15] [446262a] Update NATS-BENCH to version 1.0

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docs/NAS-Bench-201-PURE.md
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# [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
**Since our NAS-BENCH-201 has been extended to NATS-Bench, this `README` is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**
We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
Each edge here is associated with an operation selected from a predefined operation set.
@@ -172,7 +174,7 @@ api.get_more_info(112, 'ImageNet16-120', None, hp='200', is_random=True)
If you find that NAS-Bench-201 helps your research, please consider citing it:
```
@inproceedings{dong2020nasbench201,
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},

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docs/NAS-Bench-201.md
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# [NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search](https://openreview.net/forum?id=HJxyZkBKDr)
**Since our NAS-BENCH-201 has been extended to NATS-Bench, this README is deprecated and not maintained. Please use [NATS-Bench](https://github.com/D-X-Y/AutoDL-Projects/blob/master/docs/NATS-Bench.md), which has 5x more architecture information and faster API than NAS-BENCH-201.**
We propose an algorithm-agnostic NAS benchmark (NAS-Bench-201) with a fixed search space, which provides a unified benchmark for almost any up-to-date NAS algorithms.
The design of our search space is inspired by that used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph.
Each edge here is associated with an operation selected from a predefined operation set.
@@ -243,7 +245,7 @@ In commands [1-6], the first args `cifar10` indicates the dataset name, the seco
If you find that NAS-Bench-201 helps your research, please consider citing it:
```
@inproceedings{dong2020nasbench201,
title = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},