{
    "Summary": "The paper introduces a multi-scale grid-based noise adaptation mechanism to enhance diffusion models for low-dimensional datasets. The proposed method employs coarse (5x5) and fine (20x20) grids to adjust noise levels dynamically during the diffusion process. The approach is evaluated on four 2D datasets, showing significant improvements in sample quality and distribution matching compared to standard diffusion models.",
    "Strengths": [
        "Addresses a relevant problem of applying diffusion models to low-dimensional data.",
        "Proposes a novel multi-scale grid-based noise adaptation mechanism.",
        "Incorporates L1 regularization to prevent overfitting in fine-grained noise adjustments.",
        "Demonstrates significant improvements in terms of KL divergence and sample quality on four 2D datasets."
    ],
    "Weaknesses": [
        "The writing lacks clarity and is sometimes difficult to follow.",
        "Limited discussion and comparison with more diverse baselines.",
        "The novelty of the contributions could be better highlighted and differentiated from existing work.",
        "Insufficient discussion on potential societal impacts and ethical considerations.",
        "Figures and visualizations are not adequately described or included in the review.",
        "Increased computational complexity and training time due to additional grid parameters.",
        "Optimal grid sizes and regularization strength may vary depending on the dataset, requiring tuning.",
        "Effectiveness on higher-dimensional datasets remains unexplored."
    ],
    "Originality": 3,
    "Quality": 3,
    "Clarity": 2,
    "Significance": 3,
    "Questions": [
        "Can the authors provide more details on how the grid sizes (5x5 and 20x20) were chosen? Would other sizes yield similar results?",
        "How does the proposed method perform compared to other adaptive noise scheduling techniques not mentioned in this paper?",
        "Can the authors clarify the computational cost of the multi-scale approach in more detail, particularly in terms of training time and memory usage?",
        "Can the authors provide more details on the initialization and training process of the grids?",
        "How robust is the proposed method to changes in hyperparameters such as grid sizes and regularization strength?",
        "Can the method be extended to higher-dimensional datasets, and if so, what modifications would be necessary?",
        "Can the authors provide a more detailed comparison with existing noise scheduling techniques?",
        "Have the authors considered evaluating the method on higher-dimensional datasets?",
        "Can the authors clarify the specific contributions of the coarse and fine grids in the noise adaptation mechanism?",
        "What are the potential limitations or challenges in applying this approach to higher-dimensional data?",
        "How does the proposed method compare to more recent and relevant works in the field?",
        "Can the authors provide more theoretical insights into why the multi-scale approach is effective?",
        "What are the potential practical applications of this method, and how can it be generalized beyond the tested datasets?"
    ],
    "Limitations": [
        "Increased computational complexity and training time due to the additional grid parameters.",
        "Optimal grid sizes and regularization strength may vary depending on the dataset.",
        "Effectiveness on higher-dimensional datasets remains to be explored.",
        "Potential negative societal impacts are not thoroughly discussed."
    ],
    "Ethical Concerns": false,
    "Soundness": 3,
    "Presentation": 2,
    "Contribution": 3,
    "Overall": 5,
    "Confidence": 4,
    "Decision": "Reject"
}