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: The paper presents a neural architecture that fuses 3D CNN and Swin Transformer for the task of hyperspectral image (HSI) classification. The model is tested on 3 HSI datasets and provide quantitative and qualitative results.
strengths_and_weaknesses: While the used architectures are not novel, their combined use for HSI classification is novel. The reported results are far better than the reported competitors and the qualitative samples are almost identical to the ground truth.

However, the paper has room for improvement.

First of all, the description of the model is not clear to me and I think important details are missing to be reproducible:
- it is not described how PCA is used to pre-process the input;
- it is not clear what the output of the model is. From the text and Fig. 1, I would have expected a single classification for image, given the global average pooling, while the model seems to predict per-pixel classification looking at Figures 2, 3, 4;
- the loss function used for training is not reported;
- Equation 1 contains several symbols that are not defined. What are W, SW, LN, Z, z, etc.?
- Fig. 1 seems to contain an error: the original swin transformer model contains a regular-window W-MSA module followed by a shifted-window SW-MSA module while the figure in the paper shows two W-MSA modules;

In addition, in the experimental results section, there are some details that should be clarified:
- What does it mean to have a 70% test set ratio? Is it the test set 30% of the data?
- Was a validation set used to define the hyperparameters?
- Was the train/val/test split the same one used by other methods too?
- Why aren't all the models shown in Fig. 2, 3, 4 reported also in Table 1?
- Why aren't the methods presented by Roy et al. (2019) in Table V reported in Table 1 for comparison? In particular, the approach proposed by Roy et al. (2019) achieves comparable accuracy with the proposed method.
suggested_changes: I encourage the authors to address the points reported above by:
- improving the description of the proposed method and adding the requested details, in particular the output format and the loss function;
- clarifying the data used for training/validation/testing;
- updating the comparison with the literature to have a fair comparison. The paper should include the methods presented in previous work (e.g. those in Roy et al. (2019)) or explain why it is not possible to compare with those methods.

The revised version of the paper could also update the text to remove some issues with the English language and the format, e.g.:
- the abstract contains "The swin transformer network attention perception, " that seems a typo;
- the intro contains: "self-attentive mechanism" which is usually referred to as "self-attention mechanism", "setting.This", "data, and Compress";
- the paper uses the term "in this letter" instead of using "in this paper" or "in this work";
- "training calendar time" meaning "training progress".
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: a network structure using CNN and SwinTransfomer is proposed for hyperspectral image classification
strengths_and_weaknesses: S1: The performance of the proposed structure is good.

W1: It is not clear to me the motivation of such a new structure design, section 2 only gives the design of the model, but did not give any explanation on it.

W2: When comparing the model, it is better to include the number of parameters of each model, the experiments in the paper does not give this information, so it is hard to say if the comparison is fair.

W3: The paper is not anonymized, the name of the author is in the URM statement.
suggested_changes: C1: I would like the author to clarify the motivation behind the model design.

C2: It would be a stronger submission if the experiments can show more details such as the number of parameters in each of the models.
review_title: null

Meta-review: metareview: Both the reviewers agree that the paper lacks clear motivation for infusing 3DCNN with the Swin transformer and misses important implementation details such as 
- how the hyperparameters of the infused model are chosen, 
- what is the loss function used to train the model, 
- how the model fairly compares against existing models, and 
- what are the issues with the existing model that this paper is trying to solve? 

I think this is a great start and authors are encouraged to clearly explain the motivation and provide enough details about the proposed architecture. 
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive