SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 15: Comparison with other methods on NYU-v2 dataset.
Method	Segmentation	Depth	Surface Normal	ΔAst% ↓	ΔAid% ↓
(Higher Better)	(Lower Better)	Angle Distance (Lower Better)	Within t0 (Higher better)
mIoU	Pix Acc	Abs Err	Rel Err	Mean	Median	11.25	22.5	30
Static (EW)	53.77	75.45	0.3845	0.1605	23.57	17.04	35.04	60.93	72.07	0.00	1.63
MGDA-UB	50.42	73.46	0.3834	0.1555	22.78	16.14	36.90	62.88	73.61	-0.38	1.26
GradNorm	53.58	75.06	0.3931	0.1663	23.44	16.98	35.11	61.11	72.24	0.99	2.62
PCGrad	53.70	75.41	0.3903	0.1607	23.43	16.97	35.16	61.19	72.28	0.16	1.79
CAGrad	53.12	75.19	0.3871	0.1599	22.53	15.88	37.42	63.50	74.17	-1.36	0.26
RGW	53.85	75.87	0.3772	0.1562	23.67	17.24	34.62	60.49	71.75	-0.62	1.03
MoCo	54.05	75.58	0.3812	0.1530	23.39	16.69	35.65	61.68	72.60	-1.47	0.18
MoDo (ours)	53.37	75.25	0.3739	0.1531	23.22	16.65	35.62	61.84	72.76	-1.59	0.07
Table 16: Summary of hyper-parameter choices for MoDo
Office-31	Office-home	NYU-v2
optimizer of xt	Adam	Adam	Adam
xt step size (αt)	1 × 10-4	5 × 10-4	2.5 × 10-4
λt step size (γt)	10-6	10-3	10-3
weight decay	10-5	10-5	10-5
batch size	128	128	8