Kati8/ICLR_2023_2025 huggingface dataset:
authors: Zhouhuaji
authors: Xiaoyu Hao
authors: Xu Liu
authors: Lingling Li
authors: Fang Liu
authors: Licheng Jiao
reviews: 
Comments: This manuscript proposed an architecture that combines diffusion model and transformer to perform time-series recognition. Among them, a diffusion model called DSDM that uses SIR to constrain the noise intensity and fixed t is proposed; a 1-D convolutional layer is introduced into the Transformer to enhance the ability for extracting local features.
Strengths: The authors compared the proposed method with several basic architectures, such as LSTM, ViT, and Mamba.
Weaknesses: The logic of the writing itself needs to be strengthened. In the Introduction, the author describes the related research on electromagnetic signal recognition using AI and deep learning in recent years (l48-58). However, the problems mentioned in the next paragraph are not problems encountered by existing methods (l59-65). It only describes the problems encountered by traditional signal recognition. Although more recent related research is introduced in A.1 and A.2, these methods have also not been analyzed and compared with the proposed method.

The DSDM part is very confusing. In particular, the description and formula in Section 2.3 do not match the algorithm in Section A.3 at all. According to the context, algorithm in Section A.3 combines the SIR which is described in Section 2.2, and I guess Section A.3 would be a more correct description of DSDM. In addition, placing the DSDM algorithm in the related work section will make readers confused whether DSDM is the method proposed by the author or an existing method.

Adding noise to the training data is a long-established method to enhance the performance of time-series signal recognition. The experiments cannot explain whether the improvement in effect comes from the denoising process of DSDM or the noise adding for training.

Since the DSDM proposed by the author constrain the step number of diffusion processes t to 1, the author should show the relevant ablation study. Including the constrain of t, not using t as input, and comparison with the original diffusion, etc. The proposed SigFormer also needs to be verified. The author mentioned that the original transformer will cause training instability, but there is no relevant experimental comparison and analysis. Papers that use 1x1 convolution to enhance the performance of the transformer on time-series recognition have not been well cited and compared, such as [a]. And paper that add condition on signal side [b] should also be included in the comparison.

Overall, the correctness and logic of the writing of this paper need to be strengthened. There is also a lack of analytical and experimental results sufficient to verify the effectiveness of the proposed method.

[a] Eldele, Emadeldeen, et al. "Tslanet: Rethinking transformers for time series representation learning." ICML (2024).
[b] Li, Yuxin, et al. "Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting." ICLR (2024).
Recommendation: 1
Reviewer confidence: 3
Soundness/correctness: 1
Clarity: 2
reviews: 
Comments: In this paper, the authors address the problem of electromagnetic signal recognition, proposing an architecture comprising multiple specialized modules. Key components include a denoising module based on a diffusion model and a transformer-based feature extraction module, SigFormer, designed for signal processing. Sevreral experiments are conducted on the RML2016 dataset to evaluate the model's performance.
Strengths: The topic is interesting.
Weaknesses: I strongly recommend that the authors thoroughly revise Section 2.3, beginning with clear definitions for each mathematical notation. Given that denoising diffusion models are well-studied, it is essential to differentiate prior work from the novel contributions of this paper.

I also recommend expanding Section 2.4 with a comprehensive review of existing approaches. This would help position the proposed methodology more effectively within the domain. For instance, one technique discussed involves patching a 1D signal with positional encoding. The authors are encouraged to reference existing applications of this technique—not necessarily limited to electromagnetic signal recognition—and to clarify the motivation for its use in this context.

A general comment: prior work should not be confined to the introduction or related work sections. I encourage the authors to cite relevant studies for each scientific claim and technique throughout the paper. Clearly distinguishing which methods contribute to the paper’s original work will aid readers in understanding its position within the field and will highlight its unique contributions.

Finally, a specific note: as both denoising diffusion models and transformers are well-researched topics, a straightforward application of these techniques to a new domain may not sufficiently establish originality. Greater emphasis on how these techniques are adapted or innovatively applied would strengthen the paper's contribution.
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 1
reviews: 
Comments: The paper introduces Diffusion SigFormer, a novel approach for recognizing time-series signals with interference by combining diffusion models for denoising and transformer architectures for classification. The approach addresses challenges in recognizing electromagnetic signals in environments with interference, using a diffusion signal denoising module (DSDM) and a signal recognition model (SigFormer). The authors tested this method on multiple datasets (RML2016.10a, RML2016.10b, and a Bluetooth signal dataset), showing the feasibility  on multiple scenarios.
Strengths: 1. This paper proposes using a diffusion-denoising model to resolve the time-series data recognition problem. Generally, using a diffusion model to separate noise makes sense.
2. The visualization shown in Figure 4 figure 5 and Table 1 shows the feasibility of the proposed method.
Weaknesses: 1. The Phase Problem is not clearly explained. When applying DDPMs to time series data, one of the biggest challenges is the phase of the generated signals. However, according to Figures 4 and 5, the phase miss-match problem between the real part and the imaginary part still remains severe. However, it seems in Table 1, that this problem are not affecting a lot. How the model overcomes the phase shift and the potential impact is not clear. 

2. For the experiment part Table 3, the authors compare SigFormer with Mamba and ViT.  Are the CNN-based model,  SigFormer and ViT all going with the DSDM process? Or does only the SigFormer go with the DSDM process, while others just go through with their original process for precision?
Recommendation: 3
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 2