Update docs
This commit is contained in:
parent
446262a5a0
commit
5bb027d976
@ -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
|
||||
|
@ -1,5 +1,7 @@
|
||||
# [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},
|
||||
|
@ -1,5 +1,7 @@
|
||||
# [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},
|
||||
|
Loading…
Reference in New Issue
Block a user