Metadata-Version: 2.1
Name: hahtorch
Version: 0.0.5
Summary: Hebbian/Anti-Hebbian Learning for Pytorch
Home-page: https://github.com/metehancekic/HaH.git
Author: Metehan Cekic
Author-email: metehancekic@ucsb.edu
License: MIT
Download-URL: https://github.com/metehancekic/HaH/archive/v_005.tar.gz
Description: ![alt text][logo]
        
        [logo]: https://github.com/metehancekic/HaH/blob/main/figs/hahblock.png
        
        **Figure 1**: HaH block for image classification DNNs. 
        
        # Hebbian/Anti-Hebbian Learning for Pytorch
        
        Official repository for the paper entitled "Towards Robust, Interpretable Neural Networks via Hebbian/anti-Hebbian Learning: A Software Framework for
        Training with Feature-Based Costs". If you have questions you can contact metehancekic [at] ucsb [dot] edu.
        
        Maintainers:
            [WCSL Lab](https://wcsl.ece.ucsb.edu), 
            [Metehan Cekic](https://www.metehancekic.com), 
            [Can Bakiskan](https://wcsl.ece.ucsb.edu/people/can-bakiskan), 
        
        ## Dependencies
        
        > numpy==1.20.2\
        > torch==1.10.2
        
        ## How to install
        
        The most recent stable version can be installed via python package installer "pip", or you can clone it from the git page.
        
        ```bash
        pip install hahtorch
        ```
        or
        
        ```bash
        git clone git@github.com:metehancekic/HaH.git
        ```
        
        ## Experiments 
        
        We used CIFAR-10 image classification to show the effectiveness of our module. We train a VGG16 in a standard fashion and train another VGG16 that contains HaHblocks with layer-wise HaHCost as a supplement. Details of our experiments can be found in our recent [paper](https://arxiv.org/abs/2202.13074)
        
        ### CIFAR10 Image Classification with VGG16 model as Backbone
        
        ![alt text][hahvgg]
        
        [hahvgg]: https://github.com/metehancekic/HaH/blob/main/figs/hahvgg.png
        
        **Figure 2**: HaH VGG16, our proposed architecture for HaH training, see [paper](https://arxiv.org/abs/2202.13074) for more detail.
        
        ![alt text][hahresults]
        
        [hahresults]: https://github.com/metehancekic/HaH/blob/main/figs/hahresults.png
        
        **Table 1**: CIFAR10 classification: Performance of the HaH trained network against different input corruptions on the test set. For all of the adversarial attacks, we use AutoAttack which is an ensemble of parameter-free attacks, see [paper](https://arxiv.org/abs/2202.13074) for more detail.
        
        ## Current Version #
        
        0.0.5
        
        ## Sources #
        
        - [PyPi page for the code](https://pypi.org/project/hahtorch/)
        
        - [Git repo for the code](https://github.com/metehancekic/HaH)
        
Keywords: Hebbian,Anti-Hebbian,Pytorch,Neuro-inspired
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
