reviews: 
Comments: This paper proposes CEIR, a new unsupervised approach to learn concept-based image representation and thus representations that are more interpretable by humans. The concepts are first generated with a prompting strategy using GPT-4. Then concept vectors are built leveraging CLIP and the well-known concept bottleneck model. CLIP serves here as a kind of supervision. Then, a VAE is used to learn a reduced-size representation. The paper proposes a large experimental study to evaluate the proposed representation using clustering and linear probing with also a comparison to the state of the art. The proposed representation provides new state-of-the-art results for clustering. Other experiments also concern the explainability part of the learned representation and the unsupervised part. A large set of appendices presents details on these experiments.
Strengths: + The paper addresses an important issue in representation learning: the ability to learn human-understandable representation without the need for a large annotated dataset.
+ The proposed idea is simple, leveraging different existing models: clip-based models, text generative models, VAE. It is a nice way to combine existing ideas in the field of representation learning and XAI.
+ The proposed approach enables state-of-the-art results on clustering tasks on different visual classification benchmarks.
+ A large experimental study is provided including an ablation study on the size of the learned representation.
Weaknesses: I have several concerns :
+ My first concern is related to the positioning of the paper compared to concept-based explainable image representation. In particular, in the XAI field, some criteria and properties have been proposed to define the concept of good explainable representation (see for instance the work of Ghorbani [here](https://arxiv.org/pdf/1902.03129.pdf)) such as meaningfulness, coherency, and importance. How the proposed approach tackles these aspects is not clear and not evaluated at all. In particular, the proposed approach is not compared to the existing state-of-the-art on concept-based explainable image representation, supervised or unsupervised. 
+ In the same vein, since the claim of the paper is to reach to human understandable image representation, an experimental study that supports this claim should be provided. In particular, some metrics (correctness, stability, plausibility) have been proposed in the XAI field to evaluate explanations without human-level studies. See for instance [this paper](https://arxiv.org/abs/2303.15632) for faithfulness and understandability criteria.
+ Some components of the proposed approach lack a clear justification and motivation for instance the GPT part compared to the use of existing explicit knowledge (e.g. Wordnet ontology or existing knowledge graphs). 
+ Some technical details are missing to evaluate the results correctly. For instance, in Table 2, what is the size of the corresponding representations ?
+ Another concern is related to written level of the paper. The paper needs a complete proofreading since it contains a lot of mistakes (bad punctuation, strange ways to indicate the references). 
+ Sometimes some references given are not good ones. For instance, in the introduction, Yang et al and Crabbé are not seminal works for concept-based representations.
Recommendation: 3: reject, not good enough
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 2 fair