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 # Experimenting with AutoEncoders
 
-Here, we (roughly) experiment with various autoencoder architectures to tease out their ability to learn meaningful representations of data in an unsupervised setting. 
+This repo (roughly) experiments with various autoencoder architectures to tease out their ability to learn meaningful representations of data in an unsupervised setting. 
 
-- Each model is trained on the 60k MNIST training set.
-- We use each model's respective encoder to create features on varying subsets of the 10k MNIST test set.
-- We use each model's respective encodings to train a linear classifier:
+- Each model is trained (unsupervised) on the 60k MNIST training set.
+- Models are evaluated via transfer learning on the 10k MNIST test set.
+- Each model's respective encodings of varying subsets of the 10k MNIST test set are input to train a linear classifier:
   - Each linear classifier is trained on increasing split sizes ([10, 100, 1000, 2000, 4000, 8000]) and evaluated on remaining subset of the 10K MNIST test set.
+  - See `autoencoders/compare.py` for details.
 - Classifier performance is measured by mulit-class accuracy and (ROC)AUC.
 
+## Notes
+
+- Most models follow the architectures from their respective papers (see `autoencoders/models/`).
+- Hyperparameter optimization is not performed on the encoder models nor the linear classifiers.
+- Model specification/configuration located in the `outputs/` directory.
+
+## Results
+
 ![](https://github.com/chris-santiago/autoencoders/blob/master/outputs/encoder-accuracy.png)
 
 ![](https://github.com/chris-santiago/autoencoders/blob/master/outputs/encoder-auc.png)