
## Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning
Tameem Adel
Keywords: 
JAIR/2024/Proceedings/15693 - Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning.pdf
Project URL: 

### 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?_

[10]

Implementation not given.

### 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?_

[3]

(6/6)

The authors use MNIST, F-MNIST, Omniglot, CIFAR-10, CIFAR-100 and ImageNet, provide citations on each but details are a bit lacking (descriptions are there for the tasks but very brief). No direct links.

### 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?_

[7]

The hyperparameters are briefly stated in section 6/6.1 but there is no acquisition, overview and many details seem to be missing.

### 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 authors measure average test set classifcation accuracy with standard error over 6 tasks over 15 runs. The authors seem to indicate using the default data split for train and testing but this is not clearly stated.

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

[7]

Requries expertise on continual learning.
