SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In this section, we compare SPA with various state-of-the-art methods for unsupervised domain adaptation (UDA) scenario, and Source Only in UDA task means the model trained only using labeled source data. We also extend SPA to semi-suerpvised domain adaptation (SSDA) scenario and conduct experiments on 1-shot and 3-shots setting. With the page limits, see supplementary material for details.
Table 1: Classification Accuracy (%) on DomainNet for unsupervised domain adaptation (inductive), using ResNet101 as backbone. The best accuracy is indicated in bold and the second best one is underlined. Note that we compare methods on the original DomainNet dataset with train/test splits in target dataset, leading to an inductive scenario.
Method	C→P	C→R	C→S	P→C	P→R	P→S	R→C	R→P	R→S	S→C	S→P	S→R	Avg.
Source Only [25]	32.7	50.6	39.4	41.1	56.8	35.0	48.6	48.8	36.1	49.0	34.8	46.1	43.3
DAN [51]	38.8	55.2	43.9	45.9	59.0	40.8	50.8	49.8	38.9	56.1	45.9	55.5	48.4
DANN [18]	37.9	54.3	44.4	41.7	55.6	36.8	50.7	50.8	40.1	55.0	45.0	54.5	47.2
BCDM [45]	38.5	53.2	43.9	42.5	54.5	38.5	51.9	51.2	40.6	53.7	46.0	53.4	47.3
MCD [66]	37.5	52.9	44.0	44.6	54.5	41.6	52.0	51.5	39.7	55.5	44.6	52.0	47.5
ADDA [73]	38.4	54.1	44.1	43.5	56.7	39.2	52.8	51.3	40.9	55.0	45.4	54.5	48.0
CDAN [52]	39.9	55.6	45.9	44.8	57.4	40.7	56.3	52.5	44.2	55.1	43.1	53.2	49.1
MCC [31]	40.1	56.5	44.9	46.9	57.7	41.4	56.0	53.7	40.6	58.2	45.1	55.9	49.7
JAN [53]	40.5	56.7	45.1	47.2	59.9	43.0	54.2	52.6	41.9	56.6	46.2	55.5	50.0
MDD [42]	42.9	59.5	47.5	48.6	59.4	42.6	58.3	53.7	46.2	58.7	46.5	57.7	51.8
SDAT [62]	41.5	57.5	47.2	47.5	58.0	41.8	56.7	53.6	43.9	58.7	48.1	57.1	51.0
Leco [80]	44.1	55.3	48.5	49.4	57.5	45.5	58.8	55.4	46.8	61.3	51.1	57.7	52.6
SPA (Ours)	54.3	70.9	56.1	59.3	71.5	51.8	64.6	59.6	52.1	66.0	57.4	70.6	61.2
Table 1 (DomainNet) show the results on DomainNet dataset. We compare SPA with the various state-of-the-art methods for inductive UDA scenario on this large-scale dataset. The inductive setting refers that we use the train/test splits of target domain data as the original dataset [57]. During the training process, we can use the train split of target domain data and then we use the test split of target domain data for testing. This setting is relatively more difficult than others with the large-scale data amount and inductive learning. Only a few of works follows this setting while we give a result comparison in this setting here. The experiments shows SPA consistently outperforms than various of DA methods with the average accuracy of  $61.2\%$ , which is  $8.6\%$  higher than the recent work Leco [80], demonstrating the superiority of SPA in this inductive setting.
Table 2: Classification Accuracy (%) on OfficeHome for unsupervised domain adaptation, using ResNet50 as backbone. The best accuracy is indicated in bold and the second best one is underlined.
Method	A→C	A→P	A→R	C→A	C→P	C→R	P→A	P→C	P→R	R→A	R→C	R→P	Avg.
Source Only [25]	34.9	50.0	58.0	37.4	41.9	46.2	38.5	31.2	60.4	53.9	41.2	59.9	46.1
DANN [18]	45.6	59.3	70.1	47.0	58.5	60.9	46.1	43.7	68.5	63.2	51.8	76.8	57.6
CDAN [52]	50.7	70.6	76.0	57.6	70.0	70.0	57.4	50.9	77.3	70.9	56.7	81.6	65.8
BSP [10]	52.0	68.6	76.1	58.0	70.3	70.2	58.6	50.2	77.6	72.2	59.3	81.9	66.3
NPL [39]	54.1	74.1	78.4	63.3	72.8	74.0	61.7	51.0	78.9	71.9	56.6	81.9	68.2
GVB [14]	57.0	74.7	79.8	64.6	74.1	74.6	65.2	55.1	81.0	74.6	59.7	84.3	70.4
MCC [31]	56.3	77.3	80.3	67.0	77.1	77.0	66.2	55.1	81.2	73.5	57.4	84.1	71.0
BNM [13]	56.7	77.5	81.0	67.3	76.3	77.1	65.3	55.1	82.0	73.6	57.0	84.3	71.1
MetaAlign [82]	59.3	76.0	80.2	65.7	74.7	75.1	65.7	56.5	81.6	74.1	61.1	85.2	71.3
ATDOC [48]	58.3	78.8	82.3	69.4	78.2	78.2	67.1	56.0	82.7	72.0	58.2	85.5	72.2
FixBi [56]	58.1	77.3	80.4	67.7	79.5	78.1	65.8	57.9	81.7	76.4	62.9	86.7	72.7
SDAT [62]	58.2	77.1	82.2	66.3	77.6	76.8	63.3	57.0	82.2	74.9	64.7	86.0	72.2
NWD [8]	58.1	79.6	83.7	67.7	77.9	78.7	66.8	56.0	81.9	73.9	60.9	86.1	72.6
SPA (Ours)	60.4	79.7	84.5	73.6	81.3	82.1	72.2	58.0	85.2	77.4	61.0	88.1	75.3
Table 3: Classification Accuracy (%) on (a) Office31 and (b) VisDA2017 for unsupervised domain adaptation, using ResNet50 and ResNet101 as backbone respectively. For VisDA2017, all the results are based on ResNet101 except those with mark  $\dagger$ , which are based on ResNet50. The best accuracy is indicated in bold and the second best one is underlined. See supplementary material for more details.
(a) Office31	(b) VisDA2017
Method	A→D	A→W	D→A	D→W	W→A	W→D	Avg.	Method	Avg.
Source Only [25]	78.3	70.4	57.3	93.4	61.5	98.1	76.5	Source Only [25]	52.4
DANN [18]	79.7	82.0	68.2	96.9	67.4	99.1	82.2	DANN [18]	57.4
CDAN [52]	92.9	94.1	71.0	98.6	69.3	100.	87.7	CDAN [52]	73.7
MixMatch [5]	88.5	84.6	63.3	96.1	65.0	99.6	75.4	MixMatch [5]	70.4
BSP [10]	93.0	93.3	73.6	98.2	72.6	100.	88.5	BSP [10]	75.9
NPL [39]	88.7	89.1	65.8	98.1	66.6	99.6	84.7	NPL [39]	74.7
GVB [14]	95.0	94.8	73.4	98.7	73.7	100.	89.3	GVB [14]	75.3†
MCC [31]	92.1	94.0	74.9	98.5	75.3	100.	89.1	MCC [31]	78.8
BNM [13]	92.2	94.0	74.9	98.5	75.3	100.	89.2	BNM [13]	79.5
MetaAlign [82]	93.0	94.5	73.6	98.6	75.0	100.	89.2	ATDOC [48]	86.3
ATDOC [48]	95.4	94.6	77.5	98.1	77.0	99.7	90.4	FixBi [56]	87.2
FixBi [56]	95.0	96.1	78.7	99.3	79.4	100.	91.4	SDAT [62]	82.1
NWD [8]	95.4	95.2	76.4	99.1	76.5	100.	90.4	NWD [8]	83.7
SPA (Ours)	95.0	97.2	78.0	99.0	79.4	99.8	91.4	SPA (Ours)	87.7
Table 2, Table 3a, and Table 3b show the results on OfficeHome, Office31, and VisDA2017 datasets respectively. We compare SPA with the various state-of-the-art methods for transductive UDA scenario on these three public benchmarks. Table 2 (OfficeHome) shows the average accuracy of SPA is  $75.3\%$ , achieving the best accuracy, which is  $2.6\%$  higher than the second highest method FixBi [56] and  $2.7\%$  higher than the recent work NWD [8]. Note that the average accuracy reported by NWD is based on MCC [31] and SPA is based on DANN [18]. Table 3a (Office31) shows the results on the small-sized Office31 dataset, the average accuracy of SPA is  $91.4\%$ , which outperforms most of domain adaptation methods and comparable with FixBi [56]. SPA also shows a significant performance improvement over the baseline DANN. Table 3b (VisDA2017) shows the results on VisDA2017. This is a large-scale dataset with only 12 classes. SPA based on DANN achieves the accuracy of  $83.7\%$ , which have already been superior than lots of methods. However, for fair comparisons, we follow the setting of ATDOC [48] and report the accuracy based on MixMatch [5], which achieve the best accuracy of  $87.7\%$ ,  $0.5\%$  better than FixBi, and  $1.4\%$  better than ATDOC.
4.3 Model Analysis
Ablation Study. To verify the effectiveness of different components of SPA, we design several variants and compare their classification accuracy. As shown in the first section in Table 4, the w/  $\mathcal{L}_{nap}$ ,  $\mathcal{L}_{gsa}$  method is based on CDAN [52]. The difference between these variants is whether it utilize the loss of pseudo-labelling and graph spectral penalty or not. The results illustrate that both
Table 4: Classification Accuracy (\%) on OfficeHome for unsupervised domain adaptation. The table is divided into three sections corresponding to the three analysis of ablation study, robustness analysis, and parameter sensitivity, each separated by a double horizontal line. More studies on other datasets are in supplementary material.
Method	A→C	A→P	A→R	C→A	C→P	C→R	P→A	P→C	P→R	R→A	R→C	R→P	Avg.
w/o Lgsa, Lnap	54.6	74.1	78.1	63.0	72.2	74.1	61.6	52.3	79.1	72.3	57.3	82.8	68.5
w/o Lgsa	59.0	80.1	81.5	66.2	77.8	76.7	69.2	57.7	83.0	74.0	64.1	85.8	72.9
w/o Lnap	59.0	78.2	81.0	63.5	76.5	76.2	64.7	57.3	82.1	73.6	61.4	85.7	71.6
w/ Lgsa, Lnap	59.9	79.1	84.4	74.9	79.1	81.9	72.4	58.4	84.9	77.9	61.2	87.7	75.1
β = 0.1	58.3	79.2	83.2	72.8	79.3	80.4	73.3	58.2	84.5	79.0	61.4	87.3	74.7
β = 0.3	59.0	79.5	83.8	73.6	80.6	81.7	73.5	58.0	84.9	78.2	61.3	87.7	75.1
β = 0.5	59.3	79.5	84.1	73.3	80.6	81.7	73.1	58.0	84.9	78.2	62.2	87.4	75.2
β = 0.7	59.9	79.4	84.0	73.6	80.5	82.0	72.7	57.3	84.8	77.9	61.9	87.3	75.1
β = 0.9	59.7	79.6	84.7	72.9	78.7	82.2	71.2	57.5	84.5	77.2	61.7	87.5	74.8
Lrwk w/ cos	60.6	79.4	84.1	72.5	79.2	81.8	71.9	57.1	84.8	76.3	61.8	87.4	74.7
Lrwk w/ gauss	60.4	79.7	84.5	73.6	81.3	82.1	72.2	58.0	85.2	77.4	61	88.1	75.3
Lsym w/ cos	60.7	79.5	83.8	72.7	80.0	81.8	71.5	57.2	84.8	76.3	61.9	87.4	74.8
Lsym w/ gauss	59.4	79.5	84.5	73.6	81.0	81.7	72.2	57.6	84.8	77.5	61.8	87.8	75.1
Lrwk w/ k = 3	59.0	80.6	83.9	72.4	79.6	81.7	71.5	56.5	84.7	76.4	62.0	87.7	74.7
Lrwk w/ k = 5	60.4	79.7	84.5	73.6	81.3	82.1	72.2	58.0	85.2	77.4	61	88.1	75.3
Lsym w/ k = 3	59.2	80.1	84.0	72.6	81.6	81.5	71.2	57.5	84.7	76.3	61.5	87.4	74.8
Lsym w/ k = 5	59.4	79.5	84.5	73.6	81.0	81.7	72.2	57.6	84.8	77.5	61.8	87.8	75.1
the loss of pseudo-labelling and graph spectral penalty can improve the performance of baseline, and further combining these two losses together yields better outcomes, which is comparable with cutting-edge methods.
Parameter Sensitivity. To analyze the stability of SPA, we design experiments on the hyperparameter  $\beta$  of exponential moving averaging strategy for memory updates. The experimental results of  $\beta = 0.1$ , 0.3, 0.5, 0.7, 0.9 is shown in the second section of Table 4. These results are based on CDAN [52]. From the series of results, we can find that in OfficeHome dataset, the choice of  $\beta = 0.5$  outperforms than others. In addition, the differences between these results are within  $0.5\%$ , which means that SPA is insensitive to this hyperparameter.
Robustness Analysis. To further identify the robustness of SPA to different graph structures, we conduct experiments on different types of Laplacian matrix, similarity metric of graph relations, and  $k$  number of nearest neighbors of KNN classification methods. Here are the two types of commonly-used Laplacian matrix [77]: the random walk laplacian matrix  $\mathbf{L}_{rwk} = \mathbf{D}^{-1}\mathbf{A}$ , and the symmetrically normalized Laplacian matrix  $\mathbf{L}_{sym} = \mathbf{I} - \mathbf{D}^{-1/2}\mathbf{A}\mathbf{D}^{-1/2}$ , where  $\mathbf{D}$  denotes the degree matrix based on the adjacency matrix  $\mathbf{A}$ . In addition, the similarity metric are chosen from cosine similarity and Gaussian similarity, and different  $k = 3,5$  when applying KNN classification algorithm. The results are shown in the second section of Table 4. We can find that different types of Laplacian matrix still lead to comparable results. As for the similarity metric, the Gaussian similarity brings better performance than cosine similarity and the results also present that 5-NN graphs is superior than 3-NN graphs in OfficeHome dataset. For all these aforementioned experiments results, the differences between them are within  $1\%$  around, confirming the robustness of SPA.
Feature Visualization. To demonstrate the learning ability of SPA, we visualize the features of DANN [18], BSP [10], NPL [39] and SPA with the t-SNE embedding [17] under the  $\mathrm{C} \rightarrow \mathrm{R}$  setting of OfficeHome Dataset. BSP is a classic domain adaptation method which also use matrix decomposition and NPL is a classic pseudo-labeling method which also use high-confidence predictions on unlabeled data as true labels. Thus, we choose them as baselines to compare. The results are shown in Figure 2. Comparing Figure 2d with Figure 2a, Figure 2b and Figure 2c respectively, we make some important observations: (i)-we find that the clusters of features in Figure 2d are more compact, or say less diffuse than others, which suggests that the features learned by SPA are able to attain more desirable discriminative property; (ii)-we observe that red markers are more closer and overlapping with blue markers in Figure 2d, which suggests that the source features and target features learned by SPA are transferred better. These observations imply the superiority of SPA over discriminability and transferability in unsupervised domain adaptation scenario.
(a) DANN
(b) BSP
(c) NPL
Figure 2: Feature Visualization. the t-SNE plot of DANN [18], BSP [10], NPL [39], and SPA features on OfficeHome dataset in the  $\mathrm{C}\rightarrow \mathrm{R}$  setting. We use red markers for source domain features and blue markers for target domain features.
(d) SPA
Domain Adaptation. Domain adaptation (DA) problem is a specific subject of transfer learning [90, 29], and also a widely studied area in computer vision field [18, 20, 19, 73, 52, 65, 66, 40, 44, 70, 51]. By the label amount of target domain, domain adaptation can be divided into unsupervised domain adaptation, and semi-supervised domain adaptation etc. Unsupervised domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision but perform poorly when even a few labeled examples are available in the target. Our approach mainly focus on unsupervised domain adaptation, achieving the cutting-edge performance. It can also be extended to semi-supervised domain adaptation with comparable results.
There are more specifics of our baselines. Inspired by random walk [77] in graph theory, MCC [31] proposes a self-training method by optimizing the class confusion matrix. BNM [13] formulates the loss of nuclear norm, i.e., the sum of singular values based on the prediction outputs. NWD [8] calculates the difference between the nuclear norm of source prediction matrix and target prediction matrix. The aforementioned three methods all focus on the prediction outputs and establish class-to-class relations. Besides, there are two works focusing on the features, CORAL [70], measuring the difference of covariance, and BSP [10], directly performing singular value decomposition on features and calculate top  $k$  singular values. BNM, NWD and BSP are similar to our method with the use of singular values. However, our eigenvalues comes out from the graph laplacians, which contains graph topology information [12]. For example, the second-smallest non-zero laplacian eigenvalue is called as the algebraic connectivity, and its magnitude just reflects how connected graph is [12]. As we know, we are the first to propose a graph spectral alignment perspective for domain adaptation scenarios.
Self-training. Self-training is a semi-supervised learning method that enhances supervised models by generating pseudo-labels for unlabeled data based on model predictions, which has been explored in lots of works [27, 39, 50, 83, 84, 68]. These methods pick up the class with the maximum predicted probability as true labels each time the weights are updated. With filtering strategies and iterative approaches employed to improve the quality of pseudo labels, this self-training technique has also been applied to some domain adaptation methods [48, 88, 81, 37]. This kind of methods proves beneficial when the labeled data is limited. However, it relies on an assumption of the unlabeled data following the same distribution with the labeled data and requires accurate initial model predictions for precise results [59, 2]. The intra-domain alignment in our approach helps the neighbor-aware self-training mechanism generate more precise labels. The interaction between components of our approach brings our impressive results in DA scenarios.
Graph Data Mining. Graphs are widely applied in real-world applications to model pairwise interactions between objects in numerous domains such as biology [78], social media [60] and finance [86]. Because of their rich value, graph data mining has long been an important research direction [7, 16, 36, 75]. It also plays a significant role in computer vision tasks such as image retrieval [34], object detection [46], and image classification [74]. Bruna et al.[7] first introduce the graph convolution operation based on spectral graph theory. The graph filter and the eigen-decomposition of the graph Laplacian matrix inspire us to propose our graph spectral alignment.
In this paper, we present a novel spectral alignment perspective for balancing inter-domain transferability and intra-domain discriminability in UDA. We first leveraged graph structure to model the topological information of domain graphs. Next, we aligned domain graphs in their eigenspaces and propagated neighborhood information to generate pseudo-labels. The comprehensive model analysis demonstrates the superiority of our method. The current method for constructing graph spectra is only a starting point and may be inadequate for more difficult scenarios such as universal domain adaptation. Additionally, this method is currently limited to visual classification tasks, and more sophisticated and generic methods to object detection or semantic segmentation are expected in the future. Furthermore, we believe that graph data mining methods and the well-formulated properties of graph spectra should have more discussion in both domain adaptation and computer vision field.
This work is majorly supported in part by the National Key Research and Development Program of China (No. 2022YFB3304100), the NSFC under Grants (No. 62206247), and by the Fundamental Research Funds for the Central Universities (No. 226-2022-00028). JZ also thanks the sponsorship by CAAI-Huawei Open Fund.
[1] Mustapha Aouchiche and Pierre Hansen. Distance spectra of graphs: a survey. Linear algebra and its applications, 458:301-386, 2014.
[2] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Pseudolabeling and confirmation bias in deep semi-supervised learning. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
[3] Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems (NeurIPS), volume 19, 2006.
[4] Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems (NeurIPS), page 137-144, 2007.
[5] David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel. Mixmatch: A holistic approach to semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, pages 5050-5060, 2019.
[6] Andries E. Brouwer and Willem H. Haemers. Spectra of graphs. Springer Science and Business Media, 2011.
[7] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations (ICLR), 2014.
[8] Lin Chen, Huaian Chen, Zhixiang Wei, Xin Jin, Xiao Tan, Yi Jin, and Enhong Chen. Reusing the task-specific classifier as a discriminator: Discriminator-free adversarial domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7181-7190. IEEE, 2022.
[9] Liquun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, and Jingjing Liu. Graph optimal transport for cross-domain alignment. In Proceedings of the 37th International Conference on Machine Learning (ICML), pages 1542-1553. PMLR, 2020.
[10] Xinyang Chen, Sinan Wang, Mingsheng Long, and Jianmin Wang. Transferability vs. discriminability: Batch spectral penalization for adversarial domain adaptation. In Proceedings of the 36th International Conference on Machine Learning (ICML), pages 1081-1090. PMLR, 2019.
A More Setups
Hardware and Software Configurations. All experiments are conducted on a server with the following configurations:
- Operating System: Ubuntu 20.04.4 LTS
- CPU: Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz, 32 cores, 128 processors
- GPU: NVIDIA GeForce RTX 3090
More Implementation Details. We use PyTorch and tlib toolbox [28] to implement our method and fine-tune ResNet pre-trained on ImageNet [25, 26]. Following the standard protocols for unsupervised domain adaptation in previous methods [48, 56], we use the same backbone networks for fair comparisons. For Office31 and OfficeHome dataset, we use ResNet-50 as the backbone network. For VisDA2017 and DomainNet dataset, we use ResNet-101 as the backbone network. Following previous work [48], we adopt mini-batch stochastic gradient descent (SGD) to learn the feature encoder by fine-tuning from the ImageNet pre-trained model with the learning rate 0.001, and new layers, as bottleneck layer and classification layer. The learning rates of the layers trained from scratch are set to be 0.01. We use the same learning rate schedule in [48, 52], including a learning rate scheduler with a momentum of 0.9, a weight decay of 0.005, the bottleneck size of 256, and batch size of 32.
We report main experimental results with the average accuracy over 5 random trials with the initial seed 0. For transductive unsupervised domain adaptation, the reported accuracy is computed on the complete unlabeled target data, following established protocol for UDA [18, 52, 31, 10, 48]. For inductive unsupervised domain adaptation on DomainNet, the reported accuracy is computed on the provided test dataset.. We use a standard batch size of 32 for both source and target in all experiments and for all variants of our method. The reverse validation [47, 89] is conducted to select hyper-parameters. For both unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) scenarios, we fix the coefficient of  $\mathcal{L}_{nap}$  as 0.2 and the coefficient of  $\mathcal{L}_{gsa}$  as 1.0, while we will offer a sensitivity analysis for this two coefficients in the following section. More details refer to our code in the supplemental materials.
B.1 Unsupervised Domain Adaptation
In the main paper, we present the classification accuracy results on VisDA2017 dataset for unsupervised domain adaptation and leave out per-category accuracy details. In the appendix, Table 5, we give the full table on VisDA2017, using ResNet101 as backbone. Looking into this table, we can find that our SPA model consistently outperforms most of domain adaptation methods. For classic baselines, we improve DANN [18] by  $30.3\%$ , and CDAN [52] by  $14\%$ . For recent and state-of-the-art baselines, our results is  $4\%$  higher than NWD [8] and  $0.5\%$  better than FixBi [56]. Our SPA model ranks top in 6 out of 12 categories, ranks top 2 in 9 out of 12 categories, and SPA also achieves the best classification accuracy in total.
Furthermore, in the main paper, we present the classification accuracy results on original DomainNet with 365 categories. While the original DomainNet dataset has noisy labels, the previous work [64] use a subset of it that contains 126 categories from C, P, R and S, 4 domains in total, which we refer to as DomainNet126. In the appendix, Table 6, we show the results on DomainNet126. Our SPA model consistently ranks top among 12 tasks across 4 domains and achieves the best accuracy of 77.1%, which is  $12.3\%$  better than the second one. For classic baselines, we improve DANN [18] by  $20.2\%$ , and CDAN [52] by  $16\%$ .
B.2 Semi-supervised Domain Adaptation
We also extend our SPA model to semi-supervised domain adaptation (SSDA) scenario and conduct experiments on 1-shot and 3-shot setting, and  $S + T$  in SSDA task means the model trained only by the labeled source and target data.