Upload files to ''

README.md

@@ -0,0 +1,23 @@
+This is the coding part of the lecture Deep Representation Learning in PyTorch.
+
+python 3.10
+
+#### Autoencoder
+In this demo we will implement a simple autoencoder. The autoencoder will be trained on the MNIST dataset. The autoencoder will be implemented in the file autoencoder.py. The file autoencoder.py contains a class Autoencoder on the MNIST dataset.
+We compare the performance of the fully connected autoencoder with a convolutional autoencoder.
+Jupyter notebooks: 
+* autoencoder_mnist.ipynb
+* autoencoder_cifar10.ipynb
+
+#### Contractive Learning (SimCLR)
+In this demo we implemented the SimClR [1] algorithm and trained it on the cifar10 dataset.
+
+Download the pretrained encoder [here](https://cloud.cps.unileoben.ac.at/index.php/s/feHYqRHwDy7mMDm) and put it in the folder `runs`.
+
+The code was adapted from this [repo](https://github.com/sthalles/SimCLR/tree/master)
+
+Jupyter notebooks:
+* simclr.ipynb
+
+
+[1] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML'20), Vol. 119. JMLR.org, Article 149, 1597–1607.

evaluation_results.log

@@ -0,0 +1 @@
+reconstruction_loss: 0.001255071537196636linear_classification_accuracy: 0.3665knn_classification_accuracy: 0.3889clustering_ari_score: 0.02700985553219709