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Image Classification on FGVC-Aircraft (test)

95.4Accuracy

SR-GNN (Full Model)

Updated 4d ago

Evaluation Results

Method
SR-GNN (Full Model) 2022.09	95.4	-	-	-	-	0.5	-
Ensemble 2021.07	95.11	-	-	-	-	-	-
CAP 2022.09	94.9	-	-	-	-	-	-
PART 2022.12	94.6	-	-	-	-	-	-
Non-binary Transfer Learning (Optimal) 2021.07	94.5	-	-	-	-	-	-
PART 2022.12	94.4	-	-	-	-	-	-
State of the art 2021.07	93.9	-	-	-	-	-	-
L2 (Default) 2021.07	93.78	-	-	-	-	-	-
SCAPNet 2022.09	93.6	-	-	-	-	-	-
Ours 2021.01	93.42	-	-	-	-	-	-
PMG 2022.12	93.4	-	-	-	-	-	-
API-Net 2022.12	93.4	-	-	-	-	-	-
L2SP (Default) 2021.07	93.29	-	-	-	-	-	-
DCL 2021.01	93	-	-	-	-	-	-
DCL 2022.12	93	-	-	-	-	-	-
API-Net 2022.12	93	-	-	-	-	-	-
CIN 2020.03	92.8	-	-	-	-	-	-
CIN 2022.12	92.8	-	-	-	-	-	-
MoCo V2 Anisotropic 2020.11	92.71	-	-	-	-	-	-
Cross-X 2022.12	92.7	-	-	-	-	-	-
CIN 2020.03	92.6	-	-	-	-	-	-
CIN 2022.12	92.6	-	-	-	-	-	-
TASN 2021.01	92.56	-	-	-	-	-	-
ACNet 2021.01	92.4	-	-	-	-	-	-
ACNet 2022.12	92.4	-	-	-	-	-	-
DFL-CNN 2020.03	92	-	-	-	-	-	-
DFL-CNN 2022.12	92	-	-	-	-	-	-
CBAM 2021.01	91.91	-	-	-	-	-	-
DFL 2021.01	91.73	-	-	-	-	-	-
MoCo V2 2020.11	91.57	-	-	-	-	-	-
Ours 2021.01	91.55	-	-	-	-	-	-
ACNet 2022.12	91.5	-	-	-	-	-	-
NTS 2021.01	91.48	-	-	-	-	-	-
NTS 2020.03	91.4	-	-	-	-	-	-
NTSNet 2022.12	91.4	-	-	-	-	-	-
SENet 2021.01	91.37	-	-	-	-	-	-
Supervised (Reproduced) 2020.11	90.88	-	-	-	-	-	-
TASN 2021.01	90.83	-	-	-	-	-	-
SaSPA + CutMix 2024.06	90.79	-	-	-	-	-	-
SaSPA + CutMix 2024.06	90.79	-	-	-	-	-	-
SaSPA 2024.06	90.59	-	-	-	-	-	-
SaSPA 2024.06	90.59	-	-	-	-	-	-
MA-CNN 2021.01	90.35	-	-	-	-	-	-
CBAM 2021.01	90.32	-	-	-	-	-	-
HBP 2020.03	90.3	-	-	-	-	-	-
HBP 2022.12	90.3	-	-	-	-	-	-
Diff-Mix 2024.06	90.25	-	-	-	-	-	-
Diff-Mix 2024.06	90.25	-	-	-	-	-	-
SENet 2021.01	90.12	-	-	-	-	-	-
Diff-Mix + CutMix 2024.06	90.01	-	-	-	-	-	-
Diff-Mix + CutMix 2024.06	90.01	-	-	-	-	-	-
MA-CNN 2020.03	89.9	-	-	-	-	-	-
MA-CNN 2022.12	89.9	-	-	-	-	-	-
MaxEnt 2022.12	89.8	-	-	-	-	-	-
iSQRT-COV(8k) 2020.03	89.5	-	-	-	-	-	-
CutMix 2024.06	89.44	-	-	-	-	-	-
CutMix 2024.06	89.44	-	-	-	-	-	-
Mixup 2024.06	89.41	-	-	-	-	-	-
PC 2022.12	89.2	-	-	-	-	-	-
Baseline 2024.06	89.09	-	-	-	-	-	-
Real-guidance 2024.06	89.07	-	-	-	-	-	-
ReLICv2 2022.01	88.7	-	-	-	-	-	-
Real-filtering 2024.06	88.54	-	-	-	-	-	-
Boost-CNN 2020.03	88.5	-	-	-	-	-	-
RA-CNN 2021.01	88.2	-	-	-	-	-	-
RA-CNN 2022.12	88.2	-	-	-	-	-	-
SimCLR 2022.01	88.1	-	-	-	-	-	-
BYOL 2022.01	88.1	-	-	-	-	-	-
SimCLR 2020.11	88.1	-	-	-	-	-	-
BYOL 2020.11	88.1	-	-	-	-	-	-
DA-fusion 2024.06	87.64	-	-	-	-	-	-
Supervised-IN 2022.01	86	-	-	-	-	-	-
Random Init 2022.01	85.9	-	-	-	-	-	-
CGC 2021.10	85.72	-	-	-	-	-	-
SaSPA + CutMix 2024.06	85.72	-	-	-	-	-	-
SaSPA + CutMix 2024.06	85.72	-	-	-	-	-	-
SaSPA 2024.06	85.48	-	-	-	-	-	-
SaSPA 2024.06	85.48	-	-	-	-	-	-
MMCL 2021.12	85.38	-	-	-	-	-	-
Diff-Mix + CutMix 2024.06	85.12	-	-	-	-	-	-
Diff-Mix + CutMix 2024.06	85.12	-	-	-	-	-	-
Diff-Mix 2024.06	84.33	-	-	-	-	-	-
Diff-Mix 2024.06	84.33	-	-	-	-	-	-
Mixup 2024.06	84.31	-	-	-	-	-	-
Bilinear 2022.12	84.1	-	-	-	-	-	-
Baseline 2021.10	83.65	-	-	-	-	-	-
Supervised 2021.12	83.5	-	-	-	-	-	-
CutMix 2024.06	83.5	-	-	-	-	-	-
Baseline 2024.06	83.5	-	-	-	-	-	-
CutMix 2024.06	83.5	-	-	-	-	-	-
Real-guidance 2024.06	83.17	-	-	-	-	-	-
Real-filtering 2024.06	83.07	-	-	-	-	-	-
MOCHI 2021.12	83.03	-	-	-	-	-	-
MoCo-v2 2021.12	82.46	-	-	-	-	-	-
DA-fusion 2024.06	81.88	-	-	-	-	-	-
TiF learner w/o c 2024.03	80.4	-	-	-	-	-	-
TiF learner 2024.03	79.9	-	-	-	-	-	-
PCL-v2 2021.12	79.37	-	-	-	-	-	-
MoCo 2021.12	75.61	-	-	-	-	-	-
PCL-v1 2021.12	74.97	-	-	-	-	-	-

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