Kati8/ICLR_2023_2025 huggingface dataset:
authors: Qichao Shentu
authors: Beibu Li
authors: Kai Zhao
authors: Yang Shu
authors: Zhongwen Rao
authors: Lujia Pan
authors: Bin Yang
authors: Chenjuan Guo
reviews: 
Comments: The author propose a solution for general time series anomaly detection, which is call time series anomaly Detector with Adaptive bottlenecks and Dual Adversarial decoders (DADA). This model is  pre-trained on multi-domain time series data. Two components called Adaptive Bottlenecks and Dual Adversarial Decoders is leveraged to enhance the model performance. The model achieves competitive performance on various datasets even compared with SOTA models trained specifically for each dataset.
Strengths: + The task on zero-shot time series anomaly detection is quite challenging, the author build a unified end-to-end solution and demonstrate great results. 

+ The paper is well-organized, and it introduces the background and motivation of the proposed methods clearly. The figures also explain their method pretty well.

+ The paper demonstrate its experiment results on enough datasets.  The ablation studies on Sec 4.3 also makes the paper convincible.
Weaknesses: - The difference between the pretrain dataset and the evaluation dataset is not listed. How you select those dataset?

- It will be great to specify some more experiment details. Are there any data preprocessing on the input data?

- What's the computation cost(overhead) of importing Adaptive Bottlenecks and Dual Adversarial Decoders? Please specify.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 4
reviews: 
Comments: The paper proposes DADA method for general time series anomaly detection, which is pre-trained on extensive multi-domain datasets and perform anomaly detection on various target scenarios without domain-specific training. DADA proposes Adaptive Bottlenecks to addresses the flexible reconstruction requirements of multi-domain data. DADA also employs Dual Adversarial Decoders amplify the decision boundaries between normal time series and common anomalies in an adversarial training way, which improves the general anomaly detection capability across different scenarios. The paper conducts extensive experiments on nine target datasets from different domains, and demonstrate that DADA achieves superior results compared with state-of-the-art models trained specifically for each dataset.
Strengths: The paper addresses the issue that each dataset in the field of time series anomaly detection requires a specifically trained model, which holds practical significance. The paper proposes a novel method DADA for zero-shot inference on the target domain after pre-training on multiple datasets from different domains. DADA proposes the Adaptive Bottlenecks to addresses the flexible reconstruction requirements of multi-domain data, along with dual adversarial decoders to enhance the clear differentiation between normal and abnormal patterns, showcasing a degree of originality. The paper has a clear structure and expression. In the experimental section of the paper, DADA demonstrates superiority over multiple state-of-the-art methods across nine datasets and provides extensive analysis to prove the necessity of each module, showcasing high quality.
Weaknesses: 1. The paper lacks an analysis of the complementary mask rate's effect on anomaly detection performance. For example, the author could add an experiment to demonstrate the changes in anomaly detection F1-score under various complementary mask rates and explain why this mask performs the best.

2. In the experimental section, Figure 4 shows that after fine-tuning, the model's performance improved from 78.48% to 79.90% on MSL and from 84.57% to 85.50% on SMD, an increase of about 1%. The data shows that the improvement of the model after fine-tuning is small.

3. In the ablation study, Table 3 shows that the model performs better when removing the AdaBN, Adversarial, and Dual Decoders modules compared to removing only the Adversarial module. The paper explains that directly maximizing the anomalous time series will increases confusion. Further explanation is needed to clarify why maximizing the anomalous time series leads to increased confusion.

4. There are labeling mistakes in the experimental results. For example, in Table 8, the R_AUC_PR value of DADA on the PSM dataset is 47.40, which is less than ModernTCN's 47.48, but 47.40 is bolded. In Table 2, the F1 metric column for the CICIDS dataset is missing a underline marking.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: This paper develops a novel general time series anomaly detection by pre-training on multi-domain time series.  The proposed adaptive bottleneck enables the general model to be applied to various target scenarios without requiring domain-specific training. To improve the general anomaly detection capability, the dual adversarial decoder is further employed to amplify the decision boundary.
Strengths: 1. The general time series anomaly detection plays a key role in the real application.
2. DADA employs the adaptive bottlenecks for multi-domain time series data by dynamically selecting suitable bottleneck sizes. 
3. The effectiveness of both AdaBN and Dual Decoders has been demonstrated in the experiments.
Weaknesses: 1. If anomaly injection is used to establish a supervised setting, this implies that all original samples(some anomalies are included) are treated as normal samples. I suggest the author add an experiment to examine performance under varying levels of anomaly contamination in the training samples.
2. During model training, two optimization losses are utilized, along with the optimization approach and any challenges encountered in the optimization process. The visualizations of loss curves for the two losses are essential for further demonstration.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 3
reviews: 
Comments: Authors propose a General time series anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders (DADA), which enables flexible selection of bottlenecks based on different data and explicitly enhances clear differentiation between normal and abnormal series. The zero-shot capability is indeed impressive.
Strengths: The writing is clear and easy to understand.
The pictures and tables in the paper are beautiful and clear.
The proposed method, DADA, can achieve zero-shot.
Weaknesses: 1. AdaBN has almost no difference from the previous moe, yet the author exaggerates their design. I think there is a question of contribution. Moreover, the author does not explain the difference from Moe in the main text nor add relevant references [a]. I think this is somewhat deceptive. 
2. The role of AdaBN is not as great as the author claims. See the analysis in question 1. There is excessive exaggeration of contribution.
[a] Shazeer, Noam, et al. "Outrageously large neural networks: The sparsely-gated mixture-of-experts layer." arXiv preprint arXiv:1701.06538 (2017).
Recommendation: 6
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 3
reviews: 
Comments: This paper proposes a new general time series anomaly detector with adaptive bottleneck and dual adversarial decoder. By pre-training on multi-domain time series data, anomaly detection is performed efficiently in different target scenarios without domain-specific training.
The overall writing of this article is good and the logic is clear。
This paper first proposes an adaptive bottleneck module to compress features into the latent space, reflecting the different information densities of multi-domain data. It solves the challenge of different requirements for appropriate information bottlenecks for different datasets.
This paper uses dual adversarial decoders to expand the robustness distinction between normal and abnormal modes, improving the general anomaly detection ability in different scenarios.
Strengths: The overall writing of this article is good and the logic is clear。
This paper first proposes an adaptive bottleneck module to compress features into the latent space, reflecting the different information densities of multi-domain data. It solves the challenge of different requirements for appropriate information bottlenecks for different datasets.
This paper uses dual adversarial decoders to expand the robustness distinction between normal and abnormal modes, improving the general anomaly detection ability in different scenarios.
Weaknesses: The adaptive router formula of the adaptive bottleneck module proposed in this paper lacks certain innovation, and its formula is similar to the routing function proposed in “PATHFORMER: MULTI-SCALE TRANSFORMERS WITH ADAPTIVE PATHWAYS FOR TIME SERIES FORECASTING”.
Some formulas in this paper lack clear explanations, which may cause some confusion when reading. For example, the reconstruction process proposed in the article is denoted as Recon(·), but there is a lack of detailed description of this process.
Recommendation: 6
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3