
## Evidential Neighborhood Contrastive Learning for Universal Domain Adaptation
Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng
Keywords: Machine Learning (ML), Computer Vision (CV)
AAAI/2022/Proceedings/20575 - Evidential Neighborhood Contrastive Learning for Universal Domain Adaptation.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[9]

The authors provide a few details on their implementation but no source/link. They state which libraries they used and provide a large detailed schematic. 

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

[2]

(3/3)

The authors use three public benchmark data sets, which they provide descriptions, statistics and citations on. A direct link to it is not present due to the missing implementation documentation.

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

[3]

The authors state the values of their hyperparameters, but do not reveal how they were acquired. The authors do an ablation study on one of the hyperparameters. 

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

[3]

The train/test splitting is deffered to the supplementary materials which are missing (AAAI limitations). Evaluation protocols are described and citations provided. The metrics are described. 

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

[5]

Requires a good understanding on CV.
