updated submission information

Author: avaamini

lab2/solutions/Part2_Debiasing_Solution.ipynb

@@ -1081,17 +1081,24 @@
         "id": "rESoXRPQo_mq"
       },
       "source": [
-        "## 2.7 Conclusion \n",
+        "## 2.7 Conclusion and submission information\n",
         "\n",
         "We encourage you to think about and maybe even address some questions raised by the approach and results outlined here:\n",
         "\n",
         "*  How does the accuracy of the DB-VAE across the four demographics compare to that of the standard CNN? Do you find this result surprising in any way?\n",
-        "*  How can the performance of the DB-VAE classifier be improved even further? We purposely did not optimize hyperparameters to leave this up to you! If you want to go further, try to optimize your model to achieve the best performance. **[Email us](mailto:[email protected]) a copy of your notebook with the 2.6 bar plot executed, and we'll give out prizes to the best performers!** \n",
+        "*  How can the performance of the DB-VAE classifier be improved even further? We purposely did not optimize hyperparameters to leave this up to you!\n",
         "*  In which applications (either related to facial detection or not!) would debiasing in this way be desired? Are there applications where you may not want to debias your model? \n",
         "* Do you think it should be necessary for companies to demonstrate that their models, particularly in the context of tasks like facial detection, are not biased? If so, do you have thoughts on how this could be standardized and implemented?\n",
         "* Do you have ideas for other ways to address issues of bias, particularly in terms of the training data?\n",
         "\n",
-        "Hopefully this lab has shed some light on a few concepts, from vision based tasks, to VAEs, to algorithmic bias. We like to think it has, but we're biased ;). \n",
+        "Try to optimize your model to achieve improved performance. **MIT students and affiliates will be eligible for prizes during the IAP offering.** To enter the competition, please [email us](mailto:[email protected]) with your name and the following:\n",
+        "\n",
+        "* Jupyter notebook with the code you used to generate your results;\n",
+        "* copy of the bar plot from section 2.6 showing the performance of your model;\n",
+        "* a description and/or diagram of the architecture and hyperparameters you used -- if there are any additional or interesting modifications you made to the template code, please include these in your description;\n",
+        "* discussion of why these modifications helped improve performance.\n",
+        "\n",
+        "Hopefully this lab has shed some light on a few concepts, from vision based tasks, to VAEs, to algorithmic bias. We like to think it has, but we're biased ;).\n",
         "\n",
         "<img src=\"https://i.ibb.co/BjLSRMM/ezgif-2-253dfd3f9097.gif\" />"
       ]