Paper: Unsupervised Cognition (ICLR-2024)

Reviewer comment (Reproducibility):
Explain more detail about translating grey image to SDR. I can't understand how normalized input values are separated to bins. For example, MNIST dataset has 1 and 0 value. So, I can't understand how you divide input into bins.

Author response:
- **Regarding the translation of a grey image to SDR**, we want to clarify that we do not use bins in that case. We have rewrote that part of the paper to make it clearer that we just take the grey value (between 0 and 255) of each pixel and normalize it to be between 0 and 1. Then, each pixel is a dimension of the SDR with a float value between 0 and 1 representing its grey value. We also want to assure you that this translation to SDR is not MNIST specific, but it works for any grey image, as we are essentially flattening the image to be one-dimensional.

Reviewer comment (Experiments):
This translation to SDR looks like manual and specified to specific dataset such as MNIST. It seems difficult to extend your algorithm to high-resolution and diverse dataset even about CIFAR-10. This leads to the question of what the performance of the proposed method will be in situations where it is difficult to create an SDR.

Author response:
- **Regarding the translation of a grey image to SDR**, we want to clarify that we do not use bins in that case. We have rewrote that part of the paper to make it clearer that we just take the grey value (between 0 and 255) of each pixel and normalize it to be between 0 and 1. Then, each pixel is a dimension of the SDR with a float value between 0 and 1 representing its grey value. We also want to assure you that this translation to SDR is not MNIST specific, but it works for any grey image, as we are essentially flattening the image to be one-dimensional.

Reviewer comment (Novelty):
Rather than using your algorithm, DINO [1] uses classwise clustering, and CAUSE [2] does semantic segmentation with unsupervised. I don't know the advantage of your algorithm when compared.

Author response:
- **Regarding the comparison with DINO and CAUSE**, the main difference is the underlying technology, as they use transformers and neural networks and we use our own algorithm. This in fact leads to very different capabilities, with our proposal being able to say "I do not know" or being explainable, for example. Thus, the advantage of our algorithm compared to DINO and CAUSE is that it is a totally different and novel approach to unsupervised classification, with a higher potential for improvement due to its raw state. For comparison, DINO and CAUSE use already established and polished technology, and thus they have lower potential for improvement. Moreover, our novel approach has properties that DINO and CAUSE (and neural networks in general) do not have, like its explainability, the potential to avoid catastrophic forgetting (although we recognize that this last claim has not been proven neither tested in our paper, we think it will derive from the fact that different knowledge will produce different footprints, and thus they will not override previously learned footprints), or the potential to be safe against adversarial attacks (although we know that this last claim has not been proven neither tested in our paper, we think it will derive from the fact that we do not use backpropagation for learning, and thus it cannot be attacked with current adversarial attack techniques).

Reviewer comment (Writing):
You tested proposed method on cognition capability task. But, raising such a problem does not seem persuasive. I don't think it's enough to make readers understand why the problem is important in the field of deep learning. I think it would be good to give some examples.

Author response:
- **Regarding the proposed method on cognition capability task**, we have improved the text explaining our experiment, as suggested. We hope now it is clearer why we consider it to be important for the artificial intelligence field.

Reviewer comment (Experiments):
Only possible to Grey Image. Cannot validate ImageNet with Color-version. Therefore, high-dimensional feature naturally cannot be performed.

Author response:
- **Regarding the use of grey images**, we are sorry to have misled you to think that our method is only possible over grey images. That is far from the reality. The reality is that our method can process color images with the right embodiment. However, there are multiple alternatives of color embodiment (from a simple one that splits the color image in its three channels (RGB) and treat each one like a grey image, to more complex ones that add a contrast channel, or the alpha channel, etc...), and adding them to this paper was out of the scope, as we would need much more space to not only explain the decided embodiment, but also to compare multiple alternatives. Thus, we decided to leave the color to future papers.

Reviewer comment (Novelty):
The author of DINO paper equal to be that of DeepSpectral. In other words, recent clustering method has been growing to Self-Supervised Learning since long days ago. (Previous Clustering: K-Means, Spectral, Recent Clustering Method: Self-Supervised Model (DINO, MAE, iBOT)). From these reasons, the authors of this paper should change the way introducing and holding a problem. I think the proposed method only performs in grey image, then it may be possible to conduct a task of only dealing with grey image such as medical task.

Author response:
- **Regarding DINO**, could you please explain what do you mean? We are grateful for your comments, but we cannot improve our paper with them if we cannot understand them, and your answer is too short and too unspecific for us to understand what do you mean and/or what do you expect from us, sorry.