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Age Estimation 벤치마크

Age Estimation on FGNET

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MAE 낮을수록 좋음

2.23 14.67 27.12 39.56 52 2016-08 2026-09 DEX — 3.09 (2016-08-10) DEX — 3.09 (2016-08-10) DEX — 3.09 (2016-08-10) DEX — 3.09 (2016-08-10) DEX — 3.09 (2016-08-10) DEX — 3.09 (2016-08-10) DRFs — 3.85 (2017-12-19) DRFs — 3.85 (2017-12-19) DRFs — 3.85 (2017-12-19) DRFs — 3.85 (2017-12-19) DRFs — 3.85 (2017-12-19) DRFs — 3.85 (2017-12-19) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) CMAAE-OR — 3.62 (2018-04-08) Zhu et al. (Actual) — 4.58 (2018-04-08) BridgeNet — 2.56 (2019-04-06) BridgeNet — 2.56 (2019-04-06) BridgeNet — 2.56 (2019-04-06) BridgeNet — 2.56 (2019-04-06) BridgeNet — 2.56 (2019-04-06) BridgeNet — 2.56 (2019-04-06) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) C3AE (WIKI-IMDB) — 2.95 (2019-04-10) AEBFI — 52.0 (2019-04-10) MWR — 2.23 (2022-03-24) MWR — 2.23 (2022-03-24) MWR — 2.23 (2022-03-24) MWR — 2.23 (2022-03-24) MWR — 2.23 (2022-03-24) MWR — 2.23 (2022-03-24) DEX — 3.09 (2016-08-10) BridgeNet — 2.56 (2019-04-06) MWR — 2.23 (2022-03-24)
RankModel MAE PaperCodeYear
1 MWR 2.23 Moving Window Regression: A Novel Approach to Ordinal Regression nhshin-mcl/mwr 2022
2 BridgeNet 2.56 BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation 2019
3 C3AE (WIKI-IMDB) 2.95 C3AE: Exploring the Limits of Compact Model for Age Estimation StevenBanama/C3AE 2019
4 DEX 3.09 Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks 2016
5 CMAAE-OR 3.62 Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression 2018
6 DRFs 3.85 Deep Regression Forests for Age Estimation shenwei1231/caffe-DeepRegressionForests · Kasumigaoka-Utaha/Pytorch-implementation-of-DeepRegressionForests 2017
7 Zhu et al. (Actual) 4.58 Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression 2018
8 AEBFI 52 C3AE: Exploring the Limits of Compact Model for Age Estimation StevenBanama/C3AE 2019
9 MWR 2.23 Moving Window Regression: A Novel Approach to Ordinal Regression nhshin-mcl/mwr 2022
10 BridgeNet 2.56 BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation 2019
11 C3AE (WIKI-IMDB) 2.95 C3AE: Exploring the Limits of Compact Model for Age Estimation StevenBanama/C3AE 2019
12 DEX 3.09 Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks 2016
13 CMAAE-OR 3.62 Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression 2018
14 DRFs 3.85 Deep Regression Forests for Age Estimation shenwei1231/caffe-DeepRegressionForests · Kasumigaoka-Utaha/Pytorch-implementation-of-DeepRegressionForests 2017
15 Zhu et al. (Actual) 4.58 Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression 2018
16 AEBFI 52 C3AE: Exploring the Limits of Compact Model for Age Estimation StevenBanama/C3AE 2019
17 MWR 2.23 Moving Window Regression: A Novel Approach to Ordinal Regression nhshin-mcl/mwr 2022
18 BridgeNet 2.56 BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation 2019
19 C3AE (WIKI-IMDB) 2.95 C3AE: Exploring the Limits of Compact Model for Age Estimation StevenBanama/C3AE 2019
20 DEX 3.09 Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks 2016
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