libauc.models
=====================

This module aims to provide several popular deep neural network implementations collected and adapted from public codebases. We recommend users to cite the original papers when using these models. Here is an overview of this module:


.. list-table::

   * - **Model**
     - **Reference**
   * - :obj:`~libauc.models.densenet.DenseNet`: DenseNet
     - `huang2017densely <https://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf>`__    
   * - :obj:`~libauc.models.resnet.ResNet`: ResNet
     - `he2016deep <https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf>`__    
   * - :obj:`~libauc.models.resnet_cifar.ResNet`: ResNet (Cifar version)
     - `he2016deep <https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf>`__    
   * - :obj:`~libauc.models.neumf.NeuMF`: NeuMF
     - `he2017neural <https://arxiv.org/abs/1708.05031>`__    
   * - :obj:`~libauc.models.perceptron.MLP`: MultiLayer Perceptron
     - `yuan2023libauc <https://arxiv.org/abs/2306.03065>`__ (our implementation)   
       

Please refer to the source code for more details about each implementation. 
       

libauc.models.densenet
-----------------------------

.. automodule:: libauc.models.densenet
   :members:
   :undoc-members:
   

libauc.models.neumf 
--------------------------

.. automodule:: libauc.models.neumf
   :members:
   :undoc-members:

libauc.models.perceptron
-------------------------------

.. automodule:: libauc.models.perceptron
   :members:
   :undoc-members:

libauc.models.resnet
---------------------------

.. automodule:: libauc.models.resnet
   :members:
   :undoc-members:

libauc.models.resnet\_cifar
----------------------------------

.. automodule:: libauc.models.resnet_cifar
   :members:
   :undoc-members: