Sung Hwang, Kaist, South Korea, Hayeon Lee, Eunyoung Hyung
We lifted 19 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| HayeonLee/MetaD2A | canonical | 1 of 9 |
| D-X-Y/AutoDL-Projects | canonical | 0 of 5 |
| copy not recorded | — | 1 of 1 |
| yuhuixu1993/PC-DARTS | — | 0 of 3 |
| xiangning-chen/DrNAS | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| channel_shuffle | Ran | this paper's copy was not recorded; identical code first harvested from ddghost/new_darts pointer only · get_code("b9da06d4f527dd6c") |
| collate_fn | Ran | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/loader.py code served (permissive licence) · get_code("f55c31d782e48ffb") |
| Architect | Not yet run | xiangning-chen/DrNAS/architect.py pointer only (licence: NONE) · get_code("461bb7a03f47f0ad") |
| Cell | Not yet run | yuhuixu1993/PC-DARTS/model_search.py pointer only (licence: NONE) · get_code("18a2cee21566ce44") |
| MixedOp | Not yet run | yuhuixu1993/PC-DARTS/model_search.py pointer only (licence: NONE) · get_code("f58cf0ad967f722d") |
| Network | Not yet run | yuhuixu1993/PC-DARTS/model_search.py pointer only (licence: NONE) · get_code("4b9ef6a87294337c") |
| backward_step_unrolled | Not yet run | D-X-Y/AutoDL-Projects/exps/NAS-Bench-201-algos/DARTS-V2.py code served (permissive licence) · get_code("490b5c41400816e1") |
| decode_ofa_mbv3_to_igraph | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/utils.py code served (permissive licence) · get_code("7b60cfea1c582309") |
| get_loader | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/database/metaloader.py code served (permissive licence) · get_code("14ef56df31b716ed") |
| get_meta_test_loader | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/loader.py code served (permissive licence) · get_code("2d30a7e1cec63917") |
| get_meta_train_loader | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/loader.py code served (permissive licence) · get_code("75092bae3fb86ad0") |
| get_transform | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/process_dataset.py code served (permissive licence) · get_code("f2c3cad885f3c5a6") |
| is_valid_ofa_mbv3 | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/utils.py code served (permissive licence) · get_code("0de0dc03a084ba91") |
| load_graph_config | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/utils.py code served (permissive licence) · get_code("dac9f0aab7051ab2") |
| mutate_arch_func | Not yet run | D-X-Y/AutoDL-Projects/exps/NAS-Bench-201-algos/R_EA.py code served (permissive licence) · get_code("c9db316e18458ccd") |
| random_size_func | Not yet run | D-X-Y/AutoDL-Projects/exps/NATS-algos/random_wo_share.py code served (permissive licence) · get_code("b45190c694bcb277") |
| select_action | Not yet run | D-X-Y/AutoDL-Projects/exps/NAS-Bench-201-algos/reinforce.py code served (permissive licence) · get_code("0e6f925ca93551fa") |
| str2bool | Not yet run | HayeonLee/MetaD2A/MetaD2A_mobilenetV3/parser.py code served (permissive licence) · get_code("b0717d29ccfec107") |
| train_and_eval | Not yet run | D-X-Y/AutoDL-Projects/exps/NAS-Bench-201-algos/R_EA.py code served (permissive licence) · get_code("1ab9fd355247f264") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform humandesigned networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets). Moreover, since such task-specific methods search for a neural architecture from scratch for every given task, they incur a large computational cost, which is problematic when the time and monetary budget are limited. In this paper, we propose an efficient NAS framework that is trained once on a database consisting of datasets and pretrained networks and can rapidly search for a neural architecture for a novel dataset. The proposed MetaD2A (Meta Dataset-to-Architecture) model can stochastically generate graphs (architectures) from a given set (dataset) via a cross-modal latent space learned with amortized meta-learning. Moreover, we also propose a meta-performance predictor to estimate and select the best architecture without direct training on target datasets. The experimental results demonstrate that our model meta-learned on subsets of ImageNet-1K and architectures from NAS-Bench 201 search space successfully generalizes to multiple unseen datasets including CIFAR-10 and CIFAR-100, with an average search time of 33 GPU seconds. Even under MobileNetV3 search space, MetaD2A is 5.5K times faster than NSGANetV2, a transferable NAS method, with comparable performance. We believe that the MetaD2A proposes a new research direction for rapid NAS as well as ways to utilize the knowledge from rich databases of datasets and architectures accumulated over the past years. Code is available at https://github.com/HayeonLee/MetaD2A.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.00860")
get_code_for_paper("2107.00860")
have("2107.00860")
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