Papers with Code · CC BY-SA 4.0

NAS-Bench-201, CIFAR-100

아카이브 평가 40건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
DiNAS	Accuracy (Test) 73.51 Accuracy (Val) 73.49 Search time (s) 15.36	Multi-conditioned Graph Diffusion for Neural Architecture Search 2024-03-09	코드 1
β-DARTS	Accuracy (Test) 73.51 Accuracy (Val) 73.49	$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search 2022-03-03	코드 1
Shapley-NAS	Accuracy (Test) 73.51 Accuracy (Val) 73.49	Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search 2022-06-20	코드 1
DrNAS	Accuracy (Test) 73.51 Accuracy (Val) 73.49	DrNAS: Dirichlet Neural Architecture Search 2020-06-18	코드 1
AG-Net	Accuracy (Test) 73.51 Accuracy (Val) 73.49	Learning Where To Look -- Generative NAS is Surprisingly Efficient 2022-03-16	코드 1
Λ-DARTS	Accuracy (Test) 73.51 Accuracy (Val) 73.49	$Λ$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells 2022-10-14	코드 1
IS-DARTS	Accuracy (Test) 73.51 Accuracy (Val) 73.49	IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate Importance 2023-12-19	코드 1
CR-LSO	Accuracy (Test) 73.47 Accuracy (Val) 73.44	CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks 2022-11-11	코드 1
GAEA DARTS (ERM)	Accuracy (Test) 73.43	Geometry-Aware Gradient Algorithms for Neural Architecture Search 2020-04-16	코드 1
arch2vec	Accuracy (Test) 73.37 Accuracy (Val) 73.35	Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? 2020-06-12	코드 1
α-DARTS	Accuracy (Test) 73.16 Accuracy (Val) 73.21	$α$ DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling 2022-11-18	연결 없음
TNAS	Accuracy (Test) 73.02	When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search 2022-04-11	코드 1
BaLeNAS-TF	Accuracy (Test) 72.95 Accuracy (Val) 72.67	BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule 2021-11-25	연결 없음
RF-DARTS	Accuracy (Test) 72.94 Accuracy (Val) 72.95	Differentiable Architecture Search with Random Features 2022-08-18	연결 없음
NAR	Accuracy (Test) 72.89 Accuracy (Val) 72.54	Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search 2022-01-30	코드 1
GenNAS	Accuracy (Test) 72.56 Search time (s) 1080	Generic Neural Architecture Search via Regression 2021-08-04	코드 1 · 코드 2
GAE	Accuracy (Test) 72.36 Accuracy (Val) 72.62	Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation 2022-07-22	연결 없음
PRE-NAS	Accuracy (Test) 72.02 Accuracy (Val) 71.95	PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search 2022-04-27	연결 없음
CDARTS	Accuracy (Test) 71.92 Accuracy (Val) 72.12	Cyclic Differentiable Architecture Search 2020-06-18	코드 1 · 코드 2 · 코드 3
DU-DARTS	Accuracy (Test) 71.84 Accuracy (Val) 71.88	DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search 2021-11-23	코드 1
DARTS-SaBN	Accuracy (Test) 71.56	Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity 2021-02-22	코드 1
DARTS-	Accuracy (Test) 71.53 Accuracy (Val) 71.36	DARTS-: Robustly Stepping out of Performance Collapse Without Indicators 2020-09-02	코드 1
KNAS (k=40)	Accuracy (Test) 71.05	KNAS: Green Neural Architecture Search 2021-11-26	코드 1
FairNAS	Accuracy (Test) 71.00 Accuracy (Val) 70.94 Search time (s) 9845	FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search 2019-07-03	코드 1 · 코드 2
iDARTS	Accuracy (Test) 70.83 Accuracy (Val) 70.57	iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients 2021-06-21	코드 1
GDAS	Accuracy (Test) 70.70 Accuracy (Val) 71.34 Search time (s) 28926	Searching for A Robust Neural Architecture in Four GPU Hours 2019-10-10	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6
EPE-NAS (N=10)	Accuracy (Test) 70.10 Accuracy (Val) 69.78 Search time (s) 2.3	EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search 2021-02-16	코드 1 · 코드 2
EPE-NAS (N=1000)	Accuracy (Test) 69.58 Accuracy (Val) 69.44 Search time (s) 206.2	EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search 2021-02-16	코드 1 · 코드 2
NAS-LID+RSPS	Accuracy (Test) 69.39 Accuracy (Val) 69.38	NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension 2022-11-23	코드 1
SNAS	Accuracy (Test) 69.34 Accuracy (Val) 69.69	SNAS: Stochastic Neural Architecture Search 2018-12-24	코드 1 · 코드 2