23 lines
1.2 KiB
Markdown
23 lines
1.2 KiB
Markdown
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This is the coding part of the lecture Deep Representation Learning in PyTorch.
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python 3.10
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#### Autoencoder
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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.
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We compare the performance of the fully connected autoencoder with a convolutional autoencoder.
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Jupyter notebooks:
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* autoencoder_mnist.ipynb
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* autoencoder_cifar10.ipynb
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#### Contractive Learning (SimCLR)
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In this demo we implemented the SimClR [1] algorithm and trained it on the cifar10 dataset.
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Download the pretrained encoder [here](https://cloud.cps.unileoben.ac.at/index.php/s/feHYqRHwDy7mMDm) and put it in the folder `runs`.
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The code was adapted from this [repo](https://github.com/sthalles/SimCLR/tree/master)
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Jupyter notebooks:
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* simclr.ipynb
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[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.
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