From 5bb027d97676935266ad73c6aa66134d09b589ed Mon Sep 17 00:00:00 2001 From: D-X-Y <280835372@qq.com> Date: Thu, 15 Oct 2020 22:01:26 +1100 Subject: [PATCH] Update docs --- CHANGE-LOG.md | 2 +- docs/NAS-Bench-201-PURE.md | 4 +++- docs/NAS-Bench-201.md | 4 +++- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/CHANGE-LOG.md b/CHANGE-LOG.md index 40fbc3b..950d0c5 100644 --- a/CHANGE-LOG.md +++ b/CHANGE-LOG.md @@ -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 diff --git a/docs/NAS-Bench-201-PURE.md b/docs/NAS-Bench-201-PURE.md index 896842d..8a1ac54 100644 --- a/docs/NAS-Bench-201-PURE.md +++ b/docs/NAS-Bench-201-PURE.md @@ -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}, diff --git a/docs/NAS-Bench-201.md b/docs/NAS-Bench-201.md index 4a50e2f..dc233a9 100644 --- a/docs/NAS-Bench-201.md +++ b/docs/NAS-Bench-201.md @@ -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},