===============================================
Image Regression
===============================================

We provide benchmarks of different domain adaptation algorithms on `dSprites`_ and `MPI3D`_ .
Those domain adaptation algorithms includes:

-  :ref:`MDD`

.. note::

    - ``Origin`` means the accuracy reported by the original paper.
    - ``Avg`` is the accuracy reported by Transfer-Learn.
    - ``Source Only`` refers to the model trained with data from the source domain.
    - ``Oracle`` refers to the model trained with data from the target domain.


.. note::

    Labels are all normalized to [0, 1] to eliminate the effects of diverse scale in regression values.

    We repeat experiments on DD for three times and report the average error of the ``final`` epoch.

.. _dSprites:

dSprites error on ResNet-18
---------------------------------
===========     ======  ======  ======  ======  ======  ======  ======
Methods         Avg     C → N   C → S   N → C   N → S   S → C   S → N
Source Only     0.157   0.232   0.271   0.081   0.220   0.038   0.092
DD              0.057   0.047   0.080   0.030   0.095   0.053   0.037
===========     ======  ======  ======  ======  ======  ======  ======


.. _MPI3D:

MPI3D error on ResNet-18
---------------------------------
===========     ========  ========  ========  ========  ========  ========  ========
Methods         Avg       RL → RC   RL → T    RC → RL   RC → T    T → RL    T → RC
Source Only     0.176     0.232     0.271     0.081     0.220     0.038     0.092
DD              0.030     0.086     0.029     0.057     0.189     0.131     0.087
===========     ========  ========  ========  ========  ========  ========  ========