reviews: 
Comments: This paper proposes to periodically change the hyperparameters of the deep learning models as a regularization training strategy. Apply this strategy to some regularization techniques, such as dropout and Gaussian noise injection. The author validates the strategy on three datasets: CIFAR100, FER2013 and AFFECTNet.
Strengths: 1. The paper's idea is reasonable to some extent. Drawing inspiration from prior research, it suggests that adjusting hyperparameters after a set period, defined by a certain number of forward propagations, is more effective than either consistently maintaining the same hyperparameters or adjusting them during every propagation.
2. The reported results can, to some extent, validate the proposed training technique.
Weaknesses: 1. The writing quality of this paper is subpar and hard to follow. Throughout, there are numerous grammatical, typographical, and formatting errors. In the reviewer's opinion, the manuscript does not meet the basic standards for academic writing, let alone the acceptance standards for the ICLR conference.

2. The proposal of the idea lacks theoretical or substantial experimental backing. The introduced concept of periodically updating parameters seems more like a training trick. The explanation and motivation for the idea are weak.

3. The abstract contains numerous formatting errors. There should be a space between the reference and the text, and generally, an abstract should avoid excessive citations.

4. The introduction is ambiguous. Many abbreviations aren't explained in full upon their first appearance but are clarified later. The logic is muddled, and it doesn't offer clear insights or explanations for the proposed strategy.

5. The related work section is too brief and doesn't adequately review research relevant to this paper or highlight distinctions. There's no comprehensive overview; datasets are included under related work, and even GRAD-CAM is treated as related work, despite this paper only using Grad-CAM for feature visualization without modifying it. How is this considered related work?

6. Figure 1 is ambiguous and doesn't positively contribute to clarifying the paper's motivation.

7. In section 2.2, the authors state, "In this paper, R(sk) is denoted as the difference between the mean of test F1 score and training F1 score of the kth sample and b." This suggests that test data has been used during training. The reviewer believes the reported results are not credible, violating the principle that test data should not be used for training.

8. The experimental design, validation, settings, and process descriptions are unclear. Comparisons with state-of-the-art models are missing. The datasets used are mostly toy datasets. There's a lack of validation on large-scale datasets, recent deep models, other machine learning tasks, and ablation studies.

9. The figures presented in the paper are of low resolution, with incorrect font styles. The captions are unclear, and many abbreviations are undefined, as seen in Figures 1, 2, 3, 4, and 5.

10. To the reviewer, this paper neither resembles a rigorous academic manuscript nor a neatly formatted technical report. It appears more like a hastily prepared undergraduate course report. Therefore, the reviewer believes such papers should be desk rejected to avoid burdening the review process further.
Recommendation: 1: strong reject
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 1 poor