SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 5: Accuracy (%) on ColoredM-NIST dataset (C).
C	ERM	MADG
+90	71.8±0.4	72.3±0.3
+80	72.9±0.1	74.0±0.2
-90	28.7±0.5	50.5±0.4
Average	57.8	65.6
Table 6: Accuracy(%) of the MADG model for different MDD classifiers. New  $= N_{S} - 1$ , Org.  $= \binom{N_{S}}{2}$
Data	j=Org.	j=New
VLCS	78.7	78.5
PACS	86.5	86.4
OH	71.3	70.8
TI	53.7	51.1
Weighted MDD. The proposed MADG algorithm calculates the Transfer loss as the arithmetic sum of the 'j' MDD losses. This section analyzes the effect on the model's performance using a weighted  $(w)$  arithmetic sum. The different weights used in this section are  $w_{l} = \frac{1}{N_{S}}, \forall l = 1 \dots j$  (Average) and  $w_{l} = \frac{MDD_{L}}{\sum_{l=1}^{J} MDD_{l}}$  (Dynamic). The performance of the MADG algorithm using different weights is reported in Table 8. The model with  $w_{l} = 1, \forall l = 1 \dots J$
Table 7: Accuracy(%) on OfficeHome (OH) dataset
OH	Miro	SD	MADG
A	67.0 ±0.7	64.8±0.9	68.6 ±0.5
C	56.5 ±0.9	49.9 ±1.2	55.5 ±0.2
P	79.4 ±1.4	75.6 ±1.3	79.6 ±0.3
R	81.5 ±0.5	79.1 ±0.2	81.5 ±0.4
Avg	71.1	67.4	71.3
$1 \ldots j$  performs better than other weight values as reported in Table 8.
This study presented a novel adversarial algorithm for DG called MADG. Our approach is inspired by a theoretical framework that formulates the relationship between the source and unseen domains in real-valued hypothesis space. We used the margin loss function and Rademacher complexity framework to develop a generalization bound, which is informative. The proposed bound also shows a dependency between the margin value and the generalization error, which can be easily translated into an algorithm. To this end, we developed the MADG algorithm that leverages the MDD metric with minimax optimization to learn domain-invariant features and generalize well to the unseen domain. The algorithm was studied on five well-known DG benchmark datasets and reported consistent performance across
Table 8: Accuracy(%) of the MADG with different  $w_{l}$  values. One:  $w_{l} = 1$ , Avg.=Average, Dyn.=Dynamic
One	Avg.	Dyn.
P	97.7	97.8	97.6
A	87.8	87.3	87.5
C	82.2	82.0	82.4
S	78.3	77.4	77.2
Avg	86.5	86.1	86.2
all datasets as compared to other methods. We also report several empirical analyses and ablation studies.
The research of AD is supported in part by the Prime Minister Research Fellowship, Ministry of Education, Government of India. The research of LRC is supported by the Indo-Norwegian Collaboration in Autonomous Cyber-Physical Systems (INCAPS) project: 287918 of the International Partnerships for Excellent Education, Research and Innovation (INPART) program and the LowAltitude UAV Communication and Tracking (LUCAT) project: 280835 of the IKTPLUSS program from the Research Council of Norway. The research of AK is supported by TiHAN Faculty Fellowship. VNB would like to acknowledge the support through the Govt of India SERB IMPRINT and DST ICPS funding programs for this work. We are grateful to the anonymous reviewers for the feedback that helped improved the presentation of this paper.
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