Updated readme

readme.md

@@ -1,16 +1,22 @@
-# Large Margin Loss
+# Thomas Noel's Master's Degree Project
 
-A Pytorch implementation of `Large Margin Deep Networks for Classification`
+Outlier exposure (OE) has been shown to be an effective method to improve anomaly detection performance at test time [1]. The method presented in [1] uses logit suppression via KL-divergence between the model’s softmax distribution and the uniform distribution. A potential alternative to this method is to aggregate all out-of-distribution instances into a single “outlier class” during training time.
 
-## [[arxiv]](https://arxiv.org/abs/1803.05598) [[Official TF Repo]](https://github.com/google-research/google-research/tree/master/large_margin)
+Both of these methods are compatible with a variety of loss functions. Among these, the margin loss [2]  is of interest. We propose a set of experiments considering these outlier exposure methods with cross-entropy and margin losses.
 
-<hr>
+The following is the experiment matrix that we're interested in:
 
-## Results 
+|             | **Cross-Entropy** | **Margin Loss** |
+|:------------|:------------------|:----------------|
+|**Logit Suppression**| | |
+|**Kitchen Sink**| | |
 
-Testing only `MNIST` dataset in [jupyter](mnist.ipynb)
+This draws heavily from the paper linked below.
 
+## [[arxiv]](https://arxiv.org/abs/1803.05598) [[Official TF Repo]](https://github.com/google-research/google-research/tree/master/large_margin)
 
-## TOOD
+<hr>
+
+## Results 
 
-Make paper figure
+### Coming Soon!