## Unsupervised visualization of image datasets using contrastive learning
Niklas Böhm, Philipp Berens, Dmitry Kobak
Keywords: 
ICLR/2023/Proceedings/11639 - Unsupervised visualization of image datasets using contrastive learning.pdf
Project URL: nan

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[1]

The authors provide their implementation link at the end of the introduction (https://github.com/berenslab/t-simcne). They even specify a specific branch that matches time of publication. We will consider both main and this branch as part of documentation. Readme has install notes (available on pypi), usage docs inlcuding a seperate website.

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[3]

(3/3)

CIFAR-10, CIFAR-100, citation provided. Links in code. No descriptions.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[2]

HP values stated in text in 3.3. Acquisition not relevant as its used as input for their own method. Structured overview not given.

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[1]

Metrics described in 3.3.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying solely on the available documentation?_

[4]

-
