Merge pull request #137 from yoshitomo-matsubara/dev

Update documentation

docs/source/index.rst

@@ -9,9 +9,31 @@ SC2 Benchmark documentation
 SC2: Supervised Compression for Split Computing
 ***********************************************
 
+
 .. image:: https://raw.githubusercontent.com/yoshitomo-matsubara/sc2-benchmark/main/imgs/input_vs_supervised_compression.png
   :alt: Supervised Compression for Split Computing
 
+This is the official documentation of `sc2bench <https://pypi.org/project/sc2bench/>`_ package and
+our TMLR paper, `"SC2 Benchmark: Supervised Compression for Split Computing" <https://openreview.net/forum?id=p28wv4G65d>`_.
+
+As an intermediate option between local computing and edge computing (full offloading),
+**split computing** has been attracting considerable attention from the research communities.
+
+In split computing, we split a neural network model into two sequences so that
+some elementary feature transformations are applied by the first sequence of the model on a weak mobile (local) device.
+Then, intermediate, informative features are transmitted through a wireless communication channel to
+a powerful edge server that processes the bulk part of the computation (the second sequence of the model).
+
+Input compression is an approach to save transmitted data, but it leads to transmitting information irrelevant to
+the supervised task.
+To achieve better supervised rate-distortion tradeoff, we define ***supervised compression*** as
+learning compressed representations for supervised downstream tasks such as classification, detection, or segmentation.
+Specifically for split computing, we term the problem setting **SC2** (*Supervised Compression for Split Computing*).
+
+Note that the training process can be done offline (i.e., on a single device without splitting),
+and it is different from "split learning".
+
+
 Check out the :doc:`usage` section for further information.
 
 .. toctree::

docs/source/usage.rst

@@ -9,7 +9,7 @@ Usage
 Installation
 ------------
 
-To use `sc2bench`, first install it using pip:
+To use `sc2bench <https://pypi.org/project/sc2bench/>`_, first install it using pip:
 
 .. code-block:: console