Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper proposes a Discrete-Wavelet-Transform (DWT) based token mixing block for unsupervised ill-posed image inpainting. Empirical results show that the proposed model outperforms LaMa in training time and performance. 
strengths_and_weaknesses: ### Strengths

1. Efficient token mixing block. The proposed DWT-based token mixing block is parameter-efficient and fast to train.

2. Clear explanation of methods. The paper presents a clear explanation of the proposed method, making it easy to understand.

3. Visualization of inpainting images. The proposed model clearly recovers the missing parts.

### Weaknesses

1. Writing Clarity. The paper could be carefully polished. 

2. Lack of ablation study. While the paper introduces several modifications over the baseline, including the DWT block, transpose convolutional layers, and combined loss, the authors do not conduct an ablation study to demonstrate their utility.
suggested_changes: 1. Polishing the writing. The authors are encouraged to carefully polish the paper to improve its clarity. Authors could use more text to describe their methods and motivation. The authors should provide more details about the model configuration and training settings in the appendix for better reproduction. There are typos in the abstract, like ``Discreet'' (Discrete).

2. Conducting an ablation study. The authors should conduct an ablation study to demonstrate the utility of the proposed modifications over the baseline.
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions:   This paper proses a resource-efficient way for inpainting by combining  U-Net architecture with Wavemix. The authors show their method outperforms traditional CNN and LaMa in terms of various metrics. 
strengths_and_weaknesses: Strength
1. Resource Efficiency is a key problem and this problem is well motivated in the paper. 
2. The authors provided analysis of the model with respect to mask size and randomization which provide valuable insights.  

Weakness
1. This paper in its current form is not reproducible as key details are missing regarding the implementation. 
suggested_changes: Suggested Changes:
1. Try your methodology on a different dataset to check model generalizability. 
2. Compare with other methods such as deep image prioir and other state of the art models. 
3. Connect this paper to other resource efficient architecture in the literature rather than generic comparison. 
4. Provide little bit detail about token mixing algorithm and why it helps in image in-painting.
review_title: null

Decision: Invite to revise