
## Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji
Keywords: 
ICML/2022/Proceedings/17177 - Prototype-Anchored Learning for Learning with Imperfect Annotations.pdf
Project URL: nan

### 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 pseudo code on their method in table 7. No other details given, except perhaps a hint theirs was made in PyTorch.

### 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?_

[4]

(4/4)

The authors use four benchmark data sets on which they provide citations. No statistics, direct links or descriptions given. 

### 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?_

[4]

The authors state the hyperparameter values in B.2. No acquisition or overview specified.

### 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?_

[1]

The authors report validation accuracies as mean and standard deviation over 3 random runs. The datasets have their own static splits. In table 1 and 2 its single run. 

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

[5]

Requries expertise in imperfect annotation and anchoring.
