Adversarial Robust Image Classification on CIFAR-10 (test) (Attack Specific Statistics) benchmark leaderboard · SOTA2 Research

Adversarial Robust Image Classification on CIFAR-10 (test) (Attack Specific Statistics)

94.57Clean Accuracy AvgNatural

Evaluation Results

Method	Links
Natural2023.12		94.57	86.3	0.035	18.6	9	0.436	0	0	0	0	0	0	0	0	0
BAT2023.12		86.72	72.3	0.092	60.97	33.8	0.255	49.6	22.7	0.325	47.49	19.5	0.354	48.18	20.7	0.341
AdaAD2023.12		84.73	68.1	0.114	59.7	34.8	0.246	53.82	29.3	0.285	52.3	26	0.312	50.91	24.7	0.322
SAT2023.12		84.03	63.9	0.118	56.71	26.7	0.283	49.34	21	0.332	48.99	19.9	0.352	46.44	16.8	0.385
Fair-ARD2023.12		83.81	69.4	0.112	58.41	38.5	0.251	50.91	29.9	0.296	49.96	28.3	0.312	47.97	25.1	0.338
ABSLD2023.12		83.04	68.1	0.103	59.83	40.5	0.202	54.5	36.5	0.216	51.77	32.8	0.249	50.25	31	0.256
RSLAD2023.12		82.94	66.3	0.122	59.51	34.7	0.244	54	28.5	0.276	52.51	27	0.296	51.25	25.5	0.304
FRL2023.12		82.29	64.6	0.114	55.03	37.1	0.23	49.05	31.7	0.248	47.88	30.4	0.266	46.54	28.1	0.28
TRADES2023.12		81.45	67.6	0.113	56.65	26.7	0.267	51.78	30.4	0.301	49.15	27.1	0.341	48.17	25.9	0.35
CFA2023.12		78.64	63.6	0.123	57.95	36.8	0.231	54.42	33.3	0.258	50.91	27.5	0.288	50.37	26.7	0.296