Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The authors have proposed a method to improve adversarial robustness of semantic segmentation networks with hyperspectral images (HSI). The proposed adversarial discriminator ensemble network (ADE-Net) can achieve both attack type detection and network robustification under a unified model. More specifically, the discriminator network can classify the data by attack type and feed it into their specific attack-expert ensemble network. 
strengths_and_weaknesses: Strength:
1. Paper is clearly written. Motivation is also quite clear.
2. Comparison to other related methods seems adequate.
3. Introduction of an attack type discriminator is novel.  

Weakness:
1. No comparison with other SOTA methods in the experiments.  

suggested_changes: Add other SOTA comparisons in the experimental section.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper presents an Adversarial Discriminator Ensemble Network (ADE-Net) that addresses the vulnerability of semantic segmentation models classifying hyperspectral images to adversarial examples. ADE-Net employs a discriminator network to detect the attack type and a specialized ensemble network for each attack type.
strengths_and_weaknesses: Strong aspects:

1. The paper proposes an Adversarial Discriminator Ensemble Network (ADE-Net), which combines the strengths of adversarial training and adversarial detection to defend against adversarial attacks in one model.

2. The proposed model consists of two phases, with the Discriminator Phase adding adversarial detection via attack type classification and CKA to separate mixed attack data, and the Ensemble Phase constituted by a number of networks that focus on label classification.

3. The paper reports experimental results that demonstrate the effectiveness of ADE-Net in defending against various adversarial attacks on hyperspectral image classification.
suggested_changes: In Algorithm 1, there is a potential typo in the line "for b = bach1, . . . , bachB do", where "bach" should be replaced with "batch" to indicate that batches are being processed.
review_title: null

Meta-review: metareview: This paper investigates how to improve the robustness under multiple attacks and proposes an ADE-Net scheme. Generally, it is clear and interesting. However, the experiments lack of comparison with the SOTA results. Also, it would be helpful to test the proposed scheme's accuracy if the authors provide the source codes. Meanwhile, the idea is not novel. 
confidence: 5: The area chair is absolutely certain
Decision: Invite to archive