# Commandline Options

## 1] Flow Chart

## 2] Table:

Group | Argument | Description | Value | Data Type
--- | --- | --- | --- | ---
| Tag | Experiment wandb tag. (click here for more details) | Any | string
| Seed | Experiment seed. | Any | int, float
experiment_settings | Dataset | The available datasets:1-UTKFace. (click here for more details) 2-Wine Quality. (click here for more details) | 1- utkf2- wine | string
| Normalization | Enable dataset normalization | True or False | boolean
| Dataset Training Size | Size of the training dataset. | Between 0 and the difference between the size of the dataset and the size of the test set | int
| Model Type | The available models:1- Vanilla ANN, (click here for more details) 2-Vanilla CNN. (click here for more details)3- Resnet-18. (click here for more details) | 1- vanilla_ann2- vanilla_cnn3- resnet | string
model_settings | Loss Type | The available loss functions:1- Mean squared error. (MSE)2- MSE with Cutoff (threshold)3- Inverse variance. (IV)4- Batch inverse variance. (BIV) | 1- mse2- cutoffMSE3- iv4- biv | string
| 1- Epsilon or:2- Threshold Value | The value of this argument will be tailored depending on the loss type. It has two options1- Epsilon: A parameter that prevents the BIV function from having high loss values.2- Threshold Value: The cutoff or noise threshold value of the cutoffMSE loss. | [0,+] | float
| . |  |  |
| Noise | Enabling noise addition to the labels | True or False | boolean
noise_settings | Noise Type | The available noise variance distributions:1- Uniform distribution.2- Gamma distribution. | 1- uniform2-binary_uniform3- gamma | string
| Params Type | The current baseline supports the following settings for the noise distributions:1- Uniform boundaries: Where the boundaries of the uniform are provided.2- Gamma’s parameters: Where alpha and beta are provided.3- Mean and Variance: Where the mean and variance (v) of the selected distribution should be provided to estimate the its parameters indirectly. | 1- boundaries2- alphabeta3- meanvar4- meanvar_avg | string
parmas_settings | Noise Distributions Ratio (p) | Probability function over noise variance distributions. This is to study the contribution effect of low and high noise variance distributions. | [0-1] | float
| Average Variance (X) | Average over means of the noise variance distributions (two):X = p x + (1-p) x X = average mean variance.p = probability function over noise variance distributions.= mean of the first distribution. = mean of the second distribution. | Any | float
parameters | Parameters | Parameters of the noise variance distributions:1- Uniform 2- binary_uniform3- GammaOr: and v of the noise variance distributions.Note:1- When the “Params Type” is not boundaries, the first parameter in the list (var_scale) represents a condition to enabling maximum heteroscedasticity.1- will be the heteroscedasticity scale if var_scale equal to True.2- In this case, 0 < <= 1 | 1- (, )2- (,,,)3- (,)Or:1- (var_scale, ,)2- (var_scale, ,,,)or: 2- (var_scale, ,,,)3- (,,,) | list