semvec/pypi-packages huggingface dataset:
woolang-project-generator 4.0.0
The project generator for woolang
Requires Python: >=3.6

# Woolang project generator

> This is a part of [woolang](https://github.com/woo-lang/woolang)

## How to use it

```py
from context import Project, ProjectFiles

project = Project("t", ProjectFiles(
    {
        "hello" : "frfefe"
    },
    ["data"]
)).create()

```

<hr>
gga-csm 0.0.3
This package provides the implementation of GGA (https://arxiv.org/abs/2102.12196).
Requires Python: >=3.6

# GGA-Identifying-Untrustworthy-Predictions
Code relative to "Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis"
*Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier*
Paper: https://arxiv.org/abs/2102.12196
Accepted at UAI 2021

We propose a geometric gradient analysis (GGA) of the input gradients of neural networks to detect out-of-distribution data and adversarial attacks. GGA does not require retraining of a given model. Here, we analyze and interpret the gradient of a neural network w.r.t. its input (e.g., saliency map). More precisely, for a given input sample we inspect the geometric relation among all possible saliency maps, calculated for each output class of the model. This is achieved by a pairwise calculation of the cosine similarity between saliency maps. The cosine similarites for a given input can be summarizes with cosine similiarty maps (CSMs)

The following image exemplifies how GGA can be used to differentiate different data types for a MNIST model by calculating the respective CSM for every input.

<img src="../../../Downloads/GGA-Identifying-Untrustworthy-Predictions-main/Images/CSM_MNIST.JPG">

Examples of CSMs for the "Standard" CIFAR10 model provided by RobustBench libary (https://github.com/RobustBench/robustbench) are given below:

<p float="center">
  <img src="../../../Downloads/GGA-Identifying-Untrustworthy-Predictions-main/Images/CSM_Clean_Data_CIFAR10_Model.png", width=400>
  <img src="../../../Downloads/GGA-Identifying-Untrustworthy-Predictions-main/Images/CSM_Noisy_Data_CIFAR10_Model.png", width=400>
</p>

## How to use GGA

GGA can be installed with the following command:

### Instalation
```
pip install gga-csm
```

###

To compute the cosine similarity map for a given input use the following code:

``` python
from gga.CSM import cosine_similarity_maps

csm = cosine_similarity_maps(model, x)
```

An example is also given in main.py

## Citation

If you use our method in a publication please cite:
```
@article{schwinn2021identifying,
  author    = {Leo Schwinn and
               An Nguyen and
               Ren{\'{e}} Raab and
               Leon Bungert and
               Daniel Tenbrinck and
               Dario Zanca and
               Martin Burger and
               Bjoern Eskofier},
  title     = {Identifying Untrustworthy Predictions in Neural Networks by Geometric
               Gradient Analysis},
  journal   = {CoRR},
  volume    = {abs/2102.12196},
  year      = {2021},
}
```