DGMamba: Domain Generalization via Generalized State Space Model

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-13 08:54:02

Abstract: Domain generalization (DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic complexity issues.
Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global receptive fields. Despite this, it can hardly be applied to DG to address distribution shifts, due to the hidden state issues and inappropriate scan mechanisms. In this paper, we propose a novel framework for DG, named DGMamba, that excels in strong generalizability toward unseen domains and meanwhile has the advantages of global receptive fields, and efficient linear complexity. Our DGMamba compromises two core components: Hidden State Suppressing (HSS) and Semantic-aware Patch Refining (SPR). In particular, HSS is introduced to mitigate the influence of hidden states associated with domain-specific features during output prediction. SPR strives to encourage the model to concentrate more on objects rather than context, consisting of two designs: Prior-Free Scanning (PFS), and Domain Context Interchange (DCI). Concretely, PFS aims to shuffle the non-semantic patches within images, creating more flexible and effective sequences from images, and DCI is designed to regularize Mamba with the combination of mismatched non-semantic and semantic information by fusing patches among domains. Extensive experiments on four commonly used DG benchmarks demonstrate that the proposed DGMamba achieves remarkably superior results to state-of-the-art models. The code will be made publicly available.

Main paper: 
 related work: 
 CNN-based models in DG employ convolution neural networks, e.g., Alexnet [33] and ResNet [25] , to extract stronger representations. These approaches specially designed submodules to regularize the acquired features. The most intuitive idea for DG is to minimize the empirical source risk [1, 29, 70, 80] . Domain alignment [16, 39, 63, 79, 81] constrained models to convey little domain characteristics by an extra domain discriminative network. Feature disentanglement methods [40, 64, 65, 73] aim to disentangle the features to acquire task-specific information. Another significant avenue is to augment the source data [55, 61, 86, 86, 88] , thereby providing models with more samples with diverse styles. Contrastive learning [31, 31, 31, 32, 46, 71] employed the contrastive loss function on features to reduce the gap of representation distributions in one category. Ensemble learning [7, 9, 32] utilized stochastic weight average to find a flatter minimum in loss spaces to enhance generalizability. Approaches based on meta-learning [2, 12, 13, 82] attempt to address distribution shifts during the training phase, enabling models to learn to tackle domain shifts. Despite their remarkable progress in DG and the linear complexity, the lack of global receptive fields hinders further developments of CNN-based models for boosting generalization performance [35, 44] .ViT-based models in DG leverage ViT [11] as the backbone to learn high-quality representations [49, 72] , harnessing the merit of global receptive fields inherent in ViT. Discovering that the architecture of ViT aligns better with the invariant correlations than CNN [35] , GMoE [35] utilized a generalizable mixture of experts to capture diverse attributes with different experts effectively. SD-ViT [57] attempted to tackle the overfitting in source domains by guaranteeing better prediction results from the tokens in intermediate ViT layers. TFS-ViT [49] augmented the feature styles by token replacement. Despite the inherent advantages in global receptive fields, ViT-based models suffer from the quadratic complexity with respect to the image resolution rising from the attention mechanism, leading to extra overhead of computation and memory.Mamba has been widely explored in vision task [24, 37, 42, 44, 66, 68, 77, 92] to integrate both excellence of global receptive fields and linear complexity. VMamba [44] and Vim [92] proposed visual state space models to deploy Mamba for vision tasks. PCM [77] employed Mamba in point cloud analysis by introducing merged point prompts. Unfortunately, rare research has been conducted on the generalization performance of Mamba in vision tasks. To our knowledge, this is the first time that the SSM-based model Mamba has boosted the model generalization performance. 
method: 
 In this section, we begin by introducing the concepts related to Mamba [18] , i.e., the State Space Model (SSM), and the selective scan mechanism. Based on this, we propose DGMamba to enhance the model generalization performance. As shown in Figure 3 , DG-Mamba includes two core modules: Hidden State Suppressing (HSS), and Semantic-aware Patch Refining (SPR). HSS serves to suppress the domain-specific information conveyed in the hidden states by reducing the corresponding weighting factors. SPR encourages the model to focus more on the object instead of the context. SPR includes two core elements: Prior-Free Scanning (PFS), and Domain Context Interchange (DCI). PFS augments the range of the scanning mechanism of Mamba by randomly shuffling the non-semantic patches within images, and DCI attempts to distort images by substituting context patches with those in other domains.State Space Model (SSM). Derived from linear time-invariant systems, SSM-based models endeavor to establish a correlation between signals 𝑥 (𝑡) ∈ R 𝐿 and the resultant response 𝑦 (𝑡) ∈ R 𝐿 via the hidden state ℎ(𝑡) ∈ R 𝑁 . Mathematically, these models can be represented as linear ordinary differential equations (ODEs), as denoted by Eq. ( 1):EQUATIONwhere the parameters encompass 𝐴 ∈ R 𝑁 ×𝑁 , 𝐵, 𝐶 ∈ R 𝑁 , with 𝑁 denoting the state size.When applied to deep learning, time-continuous SSMs require adjustment through discretization to align with the input sample rate. Based on the discretization methodology in [23] , the ODE depicted in Eq. ( 1) can be discretized as following:EQUATIONwhere Δ represents the sample parameter of the inputs, facilitating the discretization process. 2D Selective Scan Mechanism. In addition to the challenge posed by the inconsistency between the time-continues system and discretized signals, the characteristics of multi-media signals, such as images and videos, mismatch the architecture of the SSM-based models, which are designed to capture information within temporal signals or sequence data. As a different modal from language, images contain ample spatial information, including local texture and global shape, which may not exhibit causal correlations in sequence data. To tackle this problem, the selective scan mechanism becomes imperative. Existing methods [37, 44, 92] tend to scan images into sequence data in a fixed manner. For instance, in VMamba [44] , images are flattened into two patch sequences along row and column, respectively. VMamba models these two sequences by scanning forward and backward, respectively. Shortcomings of Mamba for DG. However, the naive Mamba network encounters challenges when confronted with distribution shifts in DG, achieving a generalization performance of 87.7% on PACS dataset, inferior to the existing DG method iDAG [30] (88.8%).In the following section, we will delve into the reason behind this performance gap and propose effective strategies to assist Mamba in tackling distribution shifts for DG. For the remaining samples, we substitute their background patches with those from diverse domains, introducing texture noise and context confusion to avoid overfitting. In addition, the Hidden State Suppressing (HSS) is introduced to reduce the importance of hidden states that comprise domain-specific information.Domain-specific information poses a great challenge for deep learning models, including the SSM-based models [23, 44, 77] . In these SSM-based models, the hidden states ℎ(𝑡) play an essential role in capturing long-range correlations by propagating historical information along the sequence data. It accumulates and propagates information from previous time steps, allowing the model to remember past states and information and thereby endowing the model with global receptive fields. Despite the significant role of hidden states in Mamba, the working mechanism of hidden states could bring about negative influences when facing distribution shifts. As shown in Figure 2 , when confronted with images from diverse domains, domain-specific information could also be accumulated or even be increased in hidden states, impeding model generalizability. To mitigate the influence of accumulated domain-specific information in hidden states when predicting 𝑦, we propose to suppress the corresponding parts in the hidden states ℎ(𝑡), which may carry domain-specific information.In order to suppress the unexpected domain-specific information conveyed in the hidden states, the initial task is to recognize the hidden states that hold these adverse factors. According to the propagation rule of hidden states in Eq. ( 2), Δ𝐴 (showing a positive correlation with Ā) dominates the transition of hidden states. While the hidden states ℎ 𝑡 serve a pivotal role when predicting the output 𝑦 𝑡 . Thus, hidden states that show stronger correlations with the genuine label should be preserved more prominently during the propagation of hidden states. Consequently, they necessitate larger propagation coefficients in Ā, while less associated hidden states require comparatively smaller coefficients in Ā. As a result, the value of Δ𝐴 is utilized to determine which hidden states will undergo suppression. Mathematically, the proposed Hidden State Suppressing (HSS) strategy can be formulated as follows:EQUATIONwhere 𝛼 ∈ [0, 0.5] represents the threshold for determining whether the hidden states should be suppressed. In this way, the hidden states, whose coefficient parameters Δ𝐴 <= 𝛼 will be suppressed by Δ𝐴, while the remaining hidden states remain the same.In addition to the proposed HSS in eliminating the adverse effect of domain-specific information, enforcing the model to pay more attention to the object rather than the context can also be an effective way to facilitate the generalization performance.From the perspective of domain-invariant learning, context and object are two basic elements. The object corresponds to the foreground, which contributes most to the classification results, remaining stationary in diverse scenarios. The context is related to domain-specific information, such as background and image style, Prior-Free Scanning. Although SSM-based models [44, 92] have demonstrated excellent performance in vision tasks, a diverse and random context environment is still essential to deploy Mamba in DG. This conclusion manifests that an effective scanning mechanism is still required to tackle the challenge posed by the non-causal correlations between image pixels or patches. Suitable scanning mechanisms [44, 77, 92] should possess the ability to break unexpected spurious correlations caused by the manually created sequences of images. Nevertheless, existing SSM-based methods [44, 77, 92] are limited to scanning images into patches in a fixed unfolding approach, as indicated in Figure 2(c ). These subjective traverse strategies could result in domain-specific information in the generated sequence, making it difficult for these models to address the distribution shifts in DG.To break the spurious correlations between patches and provide an effective scanning mechanism for DG tasks, we propose Prior-Free Scanning (PFS) to address the direction-sensitive issue in Mamba. As depicted in Figure 3 , PFS attempts to randomly shuffle the context patches that may contribute to domain-specific information in the unfolded sequence, while keeping the object patches unchanged. In particular, for the representation 𝑧 = 𝑧 𝑐 + 𝑧 𝑜 ∈ R 𝐻 ×𝑊 ×𝐶 , where 𝑧 𝑐 and 𝑧 𝑜 represent the context information and object information, respectively, the shuffled representation 𝑧 𝑝 𝑓 𝑠 after the PFS strategy can be formulated as follows:EQUATIONwhere 𝑧 𝑠 𝑐 denotes the shuffled context information by employing the 𝑆ℎ𝑢 𝑓 𝑓 𝑙𝑒 (•) function in the spatial dimension. This operation could provide Mamba with sequence data exhibiting flexible scanning direction by generating context disturbance or noise while keeping the consistent object information. As a result, it mitigates the domain-specific information brought by the manual fixed scanning strategy and breaks the spurious correlations.The proposed PFS facilitates the model to pay more attention to the object instead of the context within images. However, the context information is heterogeneous across varying domains in DG. The context patch shuffling in PFS is limited in the given scenarios, inadequate to provide sufficient diverse context information for removing the domain-specific information. Besides, the heterogeneous context patches from different domains not only exhibit diverse context information but also encompass distinct local texture characteristics.To sufficiently tackle the adverse influence of heterogeneous context and diverse local texture details, we propose to create ample context scenarios and introduce local texture noise by Domain Context Interchange (DCI). As shown in the Domain Context Interchange module in Figure 3 , DCI substitutes the image context patches with those from different domains. This operation regularizes the model on the counterfacture samples [8, 69] , i.e., the combination of semantic information in one domain and non-semantic features from different domains. This strategy further forces models to focus on the generalizable features while discarding the textual details or other domain-specific features.In implementation, DCI only performs on samples with high confidence in the classification results, while other samples remain unchanged. As the samples exhibiting high classification confidence may result in overfitting in their scenarios, replacing their context patches with those from different domains could introduce challenging context noise to generate heterogeneous context and local texture noise. While remaining samples may struggle to recognize the object from the context, conducting DCI on them could further increase the difficulty in learning generalizable presentations. Specifically, we only apply DCI to the top 20% of batch samples according to the classification confidence based on the negative cross-entropy loss. These samples with high negative cross-entropy Context Patch Identifying. To distinguish the context and object patches, we take advantage of Grad-CAM [54] as the metric to measure the contributions of different regions of images. As the regions containing the object would activate the Grad-CAM greatly, while the patches exhibiting the context possess a low value in the Grad-CAM, we split the image patches into context and object according to their values in the activation map generated by Grad-CAM. Specifically, the patches with the smallest 25% Grad-CAM values are determined as the context information 𝑧 𝑐 , while the remaining are used as object information 𝑧 𝑜 . 
experiments: 
 Dataset. Following standard protocol in DG [7, 20] , we evaluate the effectiveness of our proposed DGMamba and compare it with stateof-the-art methods in DG on four commonly used benchmarks:(1) PACS [36] includes 9991 images categorized into 7 classes exhibiting 4 styles. (2) VLCS [14] involves four datasets, totaling 10729 images distributed in 5 categories. (3) OfficeHome [60] comprises 15588 images in 65 classes from 4 datasets. (4) TerraIncognita [4] encompasses 24330 photographs of 10 kinds of animals taken at 4 diverse locations. Implementation Details. Our proposed model employs Mamba [18] as the backbone, which is pretrained on ImageNet [10] . Following VMamba [44] , the network comprises 4 blocks, each consisting of 2, 2, 4, and 2 Mamba layers, respectively. Down-sampling is incorporated before each block. Additionally, bidirectional Mamba is utilized to enable each patch to gather information from any other patches. Following the training configuration in existing DG approaches [7, 20, 35] and considering the limitations of the GPU, the model undergoes training for 10000 iterations, with a batch size of 16 for each source domain. For optimization, we employ the AdamW optimizer with betas of (0.9, 0.999) and a momentum of 0.9. We incorporate a cosine decay learning rate scheduler. The initial learning rate is searched in [3e-4, 4.5e-4]. Results on PACS Dataset. Table 1 reports the generalization performance on PACS, indicating the best generalization performance of our DGMamba across almost all these domains. Notably, DG-Mamba surpasses the SOTA methods by 2.7% in average generalization performance. Besides, on the hard-to-transfer domains, such as 'Cartoon' and 'Sketch, ' our approach beats the second-best method by 3.9% and 5.6%, respectively, demonstrating the effectiveness of our DGMamba in enhancing generalization capacity. Results on VLCS Dataset. As shown in Table 2 , our DGMamba demonstrates superior average generalization performance compared to SOTA methods. In addition, our method consistently ranks among the top three performers in three out of the four scenarios, indicating its efficacy in improving the model's generalizability.Results on OfficeHome Dataset. The results on OfficeHome are shown in Table 3 . The proposed DGMamba has achieved a significant enhancement in generalization performance in all scenarios. Remarkably, DGMamba outperforms the SOTA method by 6.4% in average generalization performance. These findings showcase the superiority of DGMamba in acquiring robust representations. Results on TerraIncognita Dataset. We provide the experiment results on TerraIncognita in Table 4 . Our proposed DGMamba attains the best generalization performance across all environments. Notably, our proposed approach achieves a 4.8% gain over the SOTA method in average generalization performance. These outcomes highlight DGMamba's outstanding ability to obtain domaininvariant representations.Effectiveness of Each Component. We conduct an ablation study on PACS to disclose the contributions of our proposed modules on the generalization performance. As indicated in Table 5 , the proposed HSS, PFS, and DCI consistently facilitate the enhancement of generalizability across almost all scenarios, demonstrating their effectiveness in capturing genuine correlations and removing domain-specific information. Specifically, in the hardest-to-transfer domain where VMamba behaves poorly, i.e., 'Sketch', HSS shows the greatest enhancement, with an increase of 2.6%. This underscores the superiority of HSS in mitigating the domain-specific information carried in hidden states. The proposed PFS could also enhance the performance in the remaining domains, underscoring that offering a more effective scanning strategy and directing the model's focus on the object are beneficial to improving generalizability. In addition, the heterogeneous context and texture noise introduced by DCI can also promote the model to shift attention to domain invariant features. Moreover, the combination of these proposed modules reaches the highest performance, indicating the necessity of these modules in yielding optimal performance enhancement. SPR at Different Layers. We insert the SPR module at different layers of DGMamba and showcase diverse performance gains in Table 6 . The shallow layers contain more spurious correlations, and thereby, should benefit more from our SPR module than the deeper layers, which hold more correlations with the prediction results. This inference is also supported by the generalization performance in Table 6 . The highest gains in generalization performance have been achieved when inserting the SPR module before Block 1.Effect of 𝛼 in HSS. The threshold 𝛼 in HSS controls the number of hidden states to be suppressed. Intuitively, more suppressed hidden states would eliminate more domain-specific information, thereby enhancing generalization performance. Figure 4 reports the generalization performance of our HSS by varying 𝛼, indicating that the best performance can be obtained by HSS when the threshold 𝛼 is set to 0.25. This observation demonstrates the effectiveness of HSS in mitigating the detrimental effect of domain-specific features on generalization. However, it is noteworthy that a relatively large 𝛼 would also mitigate the positive influences of hidden states associated with the valuable features for predictions, consequently degrading the generalization performance.Hidden State Suppressing (HSS) vs. Hidden State Masking (HSM).In practice, we also experiment with a hidden state masking strategy to eliminate the adverse effect of domain-specific information. HSM Activation Map Visualization. To further visually verify the outstanding prediction mechanism of our proposed DGMamba, we provide visualizations for the activation maps of the last state space layer with Grad-CAM techniques [54] . The results are reported in Figure 6 , demonstrating that our proposed DGMamba can still capture the semantically related information, i.e., the global shape and the object itself, when confronted with hard samples exhibiting significant disparity from the real world. Taking the 'dog' for instance, our proposed DGMamba is able to focus on the entire dog face, while the baseline VMamba suffers from recognizing these critical features. In addition, VMamba can be easily interfered by the background and the texture details, exampled by the 'house' and 'person'. While our DGMamba can still comprehensively learn the object information even with such complex domain-specific information. These findings highlight the excellence of our method in improving model generalization capacity.  
: 
 Compared with these state-of-the-art (SOTA) methods, our proposed DGMamba achieves the best trade-off between the generalization performance (Accuracy) and computational complexity (Number of parameters).effective approach for eliminating distribution shifts is domain generalization (DG) [5, 20, 85] , which attempts to encourage models to focus on semantic factors akin to humans and overlook nonessential features [17, 21, 38, 67, 69, 78, 83, 85] .A large amount of research in DG has concentrated on the design of special modules to acquire robust representation [62, 74, 78, 90] , including domain alignment [16, 30, 45, 81] , feature contrastive learning [32, 46] , and style augmentation [8, 65, 75, 83, 88] , etc. Nonetheless, prevailing DG methods heavily rely on CNNs as the backbone to extract latent features, only possessing local receptive fields. As a result, they tend to learn local details and overlook global information, impeding generalization and leading to less-desired performances on unseen domains. Recent advancements in DG [35, 49, 84] have shifted the backbone architecture from CNN [25] to ViT [11] due to its global receptive fields of selfattention layer [22, 35, 49, 50] . However, such attention layers in ViT introduce the challenge of quadratic complexity and lead to unacceptable computational inefficiency and memory overhead especially when models are very large, which makes it hard to deploy these DG methods in real-world applications.Mamba, as an emerging state space model (SSM), possesses superior linear complexity and global receptive fields. It has been recently explored in language modeling and has a promising potential in computer vision. By employing input-dependent parameters in SSM, Mamba has exhibited promising performance in sequence data modeling and capturing long-range dependencies. In particular, VMamba [44] and Vision Mamba [92] propose to traverse the spatial domain and convert any non-causal visual image into order patch sequences. However, such SSM-based models inevitably exhibit performance degradation in unseen domains due to the lack of consideration of domain shifts and tailored designs, and there still exist generalization performance gaps compared with the state-of-the-art DG methods, e.g,, iDAG [30] on the PACS dataset (87.7% vs. 88.8%). Therefore, how to improve the generalizability of Mamba-based models and investigate what hinders Mamba from addressing distribution shifts for DG is a very critical problem. In this paper, our goal is to enhance the generalizability of Mamba-like models toward unseen domains. Our motivations mainly lie in two aspects. Firstly, we observe that hidden states, as an essential part of Mamba, play an important role in modeling long-range correlations by recording the historical information in sequence data, facilitating global receptive fields. However, when dealing with unseen images containing diverse domain-specific information from varying domains, such hidden states may yield undesirable effects. The domain-specific information could potentially be accumulated or even amplified in hidden states during propagation, as indicated in Figure 2 (a), thereby degrading the generalization performance. Secondly, how to effectively scan 2D images into 1D sequence data that is suitable for Mamba in DG is still an open problem since the pixels or patches of images do not exhibit the necessary causal relations existed in sequence data. Although recent works [37, 44, 92] have explored various scanning strategies for vision tasks, such simple 1D traverse strategies may result in unexpected domain-specific information within the generated sequence data (Figure 2  
 
 Appendix:  


Decision: - Summary of Strengths and Contributions

The paper introduces an approach to domain generalization (DG) by leveraging the Mamba state space model (SSM). The authors propose two core components, Hidden State Suppressing (HSS) and Semantic-aware Patch Refining (SPR), to address issues with hidden state accumulation and inappropriate scan mechanisms in Mamba when applied to DG. The paper is well-organized, clearly presented, and provides a comprehensive evaluation through extensive experiments on four DG benchmarks, demonstrating state-of-the-art performance.

- Summary of Weaknesses and Rebuttal Gaps

Despite the strengths, several critical weaknesses were raised by the reviewers:

1. Theoretical Derivation and Experimental Protocols (Reviewer k334): Reviewer k334 raised concerns about the lack of theoretical derivation for the Prior-Free Scanning mechanism and questioned the experimental protocols used. Additionally, there were inconsistencies in reported accuracies (e.g., Naive Mamba on the PACS dataset) and the need for Grad-CAM visualization comparisons. While the rebuttal provided partial clarifications, some issues remain unresolved.

2. Generalization and Dataset Coverage (Reviewer vghi): Reviewer vghi pointed out that the original Mamba already achieved strong performance, with DGMamba showing limited improvement on some datasets. The paper lacked results for several commonly used DG datasets like SVIRO, Wilds-Camelyon, Wilds-FMOW, and DomainNet. The authors’ response addressed some concerns but did not fully resolve the need for additional experiments and validations.

3. Motivation and Module Design (Reviewer WKDQ): Reviewer WKDQ questioned the motivation behind applying Mamba to DG and the rationale for the HSS and SPR modules. The reviewer also noted that the modules seemed independent and not fully integrated with the Mamba architecture. Concerns about hyperparameter sensitivity and the need for more convincing experiments to validate the modules’ effectiveness were partially addressed in the rebuttal.

4. Process Clarification: According to line 568, “DCI only performs on samples with high confidence in the classification results, while other samples remain unchanged.” This suggests that the proposed method requires forwarding samples first and then, based on the prediction confidence, forwarding them again. Please provide clarification on this.

- Final Recommendation

Despite the aforementioned weaknesses, the paper presents a fair contribution to the field of domain generalization. The introduction of DGMamba and the extensive experimental results demonstrating its efficacy. The authors have provided reasonable modifications to the Mamba framework, showing a good performance in domain generalization.

After considering the feedback from all reviewers and the authors’ responses, I recommend accepting the paper. Please make sure the camera-ready version includes all suggested changes, clarify the concerns raised by reviewers, and re-write the discussion on potential limitations as well as future directions. This recommendation has been discussed with SAC.

Reviews: review: paper_summary: paper_summary: The paper introduces DGMamba, a novel framework for domain generalization (DG) that aims to address distribution shift problems in various scenes. Domain generalization is challenging for multimedia and computer vision systems because they struggle to recognize images with domain distribution shifts, such as changes in background and lighting conditions. Existing DG methods rely heavily on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs), which have limitations such as limited receptive fields or quadratic complexity issues. DGMamba leverages the advantages of a state space model (SSM) called Mamba, which offers superior linear complexity and global receptive fields. However, Mamba faces challenges with hidden state issues and inappropriate scan mechanisms when applied to DG. To overcome these, DGMamba incorporates two core components: Hidden State Suppressing (HSS) and Semantic-aware Patch Refining (SPR). Extensive experiments on four DG benchmarks demonstrate that DGMamba achieves superior results compared to state-of-the-art models.
strength_weakness: strength_weakness: The first work to introduce Mamba into the field of domain generalization (DG) was exceptionally comprehensive, meticulously covering both theoretical foundations and practical implementations. The experimental results presented by the author were particularly impressive, achieving state-of-the-art generalization performance across a variety of challenging datasets. These results not only demonstrated the efficacy of Mamba in enhancing generalization but also provided valuable insights into the underlying mechanisms that contribute to its success. By pushing the boundaries of what was previously thought possible, this work has opened up new avenues for research and application, offering a fresh and innovative perspective for tackling the complex challenges associated with domain generalization problems. Through rigorous experimentation and detailed analysis, the author has laid a solid groundwork for future studies, encouraging further exploration and refinement of Mamba-based approaches in the quest for more robust and adaptable machine learning models.
limitations: limitations: 1.Why can Prior-Free Scanning make images have causal correlation? Lack of corresponding theoretical derivation.
2.Were the experimental results averaged over multiple runs? What protocol was used in the experiment? The experimental section does not clearly detail these.
3. As mentioned in Table 5, VMamba has an accuracy of 89.4%, which exceeds all other comparison methods in Table 1. How can the contradiction be explained between this and the statement in the paper "MambaOut: Do We Really Need Mamba for Vision?" that says, "We analyze common visual tasks against these criteria and argue that introducing Mamba for ImageNet image classification is unnecessary"?
4. Why is the accuracy of Naive Mamba (VMamba) on the PACS dataset mentioned as 87.7% on page 3, while Table 5 lists it as 89.4%?
5. The Grad-CAM visualization experiment lacks a comparison with the use of ResNet50 and DeiT-S models.
6. The code is not open source yet. Can it be easily reproduced after being open sourced?
7. The author can try EfficientVMamba-B, Vim-S, and other models to prove that the proposed module is effective in multiple visual Mamba models.
paper_summary: paper_summary: Based on Mamba, the authors propose DGMamba to enhance the model's domain generalization. It consists of two parts: Hidden State Suppressing(HSS) and Semantic-aware Patch Refining(SPR). SPR consists of two core modules: Prior-Free Scanning (PFS) and Domain Context Interchange (DCI).
strength_weakness: strength_weakness: 1 HSS mitigates the influence of accumulated domain-specific information in hidden states. 
2 SPR assists the model in better focusing on the object.
3 It achieves state-of-the-art generalization performance.
limitations: limitations: 1 The reported results of GMoE are lower than the results publicly disclosed in its article.
2 The original Mamba has achieved the state-of-the-art generalization performance on the PACS dataset. DGMamba shows limited improvement. Has Mamba also achieved the best results on other datasets?
3 Is an additional model required for Grad-CAM? Is the Grad-CAM used only in training.
4 The article lacks results for the SVIRO, Wilds-Camelyon, Wilds-FMOW, and DomainNet datasets, which are also commonly used for evaluating domain generalization.
paper_summary: paper_summary: This paper introduces Mamba to the task of DG. The proposed method consists of two modules HSS and SPR to address the issues when applying Mamba to DG. Ths paper is well written and the description of the proposed method is clear. The overall idea in this paper is generally novel (but not too much). However, there are more concerns to the paper from the problem motivation to the rationale behind the proposed modules.
strength_weakness: strength_weakness: 1.	This paper is well written and easy to follow.
2.	How to apply the Mamba architecture to the task of DG is interesting.
limitations: limitations: 1.	The motivation of this paper is not strong. Mamba is good, the task of DG is important, which doesn’t mean that we have to apply Mamba to DG. What are the advantages of applying Mamba to DG compared to ViT? Does the state space model have any specifically good property for DG? If not, applying Mamba to DG is simply a trivial “A+B” work for pursuing the hot topic. Is section 3, the authors discussed the shortcomings of Mamba for DG, but without describing the advantages of Mamber for DG. So why must we apply Mamba to DG? Is it only because Mamba is hot and XXXMamba is easy for publication?
2.	The authors claim that the domain-specific information is accumulated in hidden states, which should be suppressed. Can this speculation be validated, because it is an important motivation to design HHS? Besides, the design of HHS is somehow heuristic. What is the retionale behind simply using a threshold to suppress delta A? 
3.	What is the reason to introduce of SPR with the existing of HSS? What problems of HHS are left behind for SPR to solve? In addition, handling the relationship between context and object seems not quite related to the architecture of Mamba. It can also be applied as a plug-in to ViT-based DG methods. Therefore, HHS and SPR seem to be indepent each other. 
4.	As an important hyperparameter, does the selection of threshold alpha vary according to the dataset? The experiment shown in Figure 4 only gives the result on PACS. 
5.	In the comparison of between HSM and HSS in Table 7, what are the results when alpha=0.3 and 0.35, because they are shown in Figure 4? 
6.	Through all the experiments, i.e. SOTA comparison, ablations, feature visualization, activation map, it is still hard to make me convinced that HHS learn more domain-generic knowledge. Besides, the experiments cannot show how and why SPR works. More specifically designed experiments for model validation is needed. Some routine experiments (hyperparameter sensitivity, computational efficiency, feature visualization, and activation map) are not quite sufficient to validate the effectiveness of the proposed modules.