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# System prepended metadata

title: Meetings Coreset Selection

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# Meetings Coreset Selection

## Meeting 2021-03-06

### Project desctiption

1. Replicate one of the baselines from Selection via Proxy for coreset selection 50%
    * Greedy K-Centers
3. Replicate one article 90%
    * GLISTER: Online + Active
5. Analyze how well selected subsets generalize across different models 100%

### Questions
* What is the scope of the testing datasets?
    * Regression + classification?
    * Multi-class classification?
    * How many parameter-tweaks?
        * Noise in the dataset
        * Size of dataset
        * Density of dataset
    * A: CIFAR-10
    * A: MNIST

* What is the scope of the models we should train with the selected core-sets?
    * How many models?
    * What kinds of models?
        * Standard: KNN, LinReg, DT, RF, LogReg
        * Bayesian: Gaussian Process

    * Reduction of datapoints with the algorithm depends on the model
    * Finding out if same reduction of dataset for one algorithm also works well for other algorithms
    
* GLISTER and Greedy-K-Centers papers have both active learning and coreset selection
    * Only do GLISTER online

* Shall various approximations of GLISTER (perfomance-related) be implemented?
    * Focus on the main idea, main algorithm first

* What about computing ressources?
    * Roland has access to Zhores Sandbox, Vladimir and Yuliya don't have any access

* What kind of algorithms do we have to compare?
    * It's only Greedy K-Centers compared with GLISTER
    * It is expected that G-KC performs slightly worse than GLISTER
    * G-KC is a known algorithm, it is expected to work. GLISTER on the other hand is new, not verified if it works

* What about the code in the repos?
    * It's OK to use the code from the repositories. The main body of work is to provide a test-interface for people to give various datasets, and output a comparison between G-KC and GLISTER

* (Yuliya) Regarding GLISTER algorithm, can we use the available codes of algorithms for comparison (FASS, BADGE)?
* (Yuliya) Regarding the GLISTER paper, should we replicate the appendix experiments?

# Meeting 2021-03-13

Next steps:
* Indices with Greedy K-Centers:
    * Train ResNet on CIFAR/MNIST and extract features from ResNet (second to last layer)
    * Use K-Centers algorithm on feature embeddings, and get core-set indices

* Indices with GLISTER:
    * Try to get own implementation of GLISTER to work
    * Build a wrapper around GLISTER
    * Use GLISTER to get core-set indices

* Use selected core-sets to re-train ResNet and check performance
* Use selected core-sets to train other nets (AlexNet) and check performance

# Meeting 2021-03-18

* What kind of functionality should the commanding front end have?
    * download weights of neural networks? No
    * core-set selectors:
        * generate latent space dataset
        * download latent space dataset
        * generate subset indices
            * parameters:
                * selection method (k-centers, gliser)
                * % of dataset
        * save indices list

* How many sub-sets of the full dataset do you want to have in the report?
    * 30, 50

* If Inception takes too much time to train, can we use densenet 121?
    * Use densenet 121

* Measurements:
    * K-Centers and Random on CIFAR-10
        * with 10%, 30%, 50%
    * K-Center and GLISTER and Random on CIFAR-10 (if we have time)
        * with 10%, 30%, 50%
        * 100 epochs training for
    * K-Center and GLISTER on smaller dataset (MNIST)
        * with 10%, 30%, 50%

# Meeting 2021-03-20

* Final report Appendix B: Do we need to comment on every question?
    * If there is some important additional information, write it down
* Plots: How do I get std/CI from one measurement?
    * not necessary
* K-Centers: Replication was done with pre-activation layer
    * it's fine with default resnet18
* Repository: Is it OK if we have test execution software in jupyter notebooks?
    * It's OK for interfaces, still would be nice to have scripts
* Report:
    * Add to introduction:
        * Hyperparameter search to find best hyperparameters. Trains network often, and for that a smaller dataset is quite important.
        * Neural architecture search: Train models many many times