publish [GPU]: course_UvA-DL/08-deep-autoencoders

Author: Azure Pipelines

.notebooks/course_UvA-DL/08-deep-autoencoders.ipynb

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.notebooks/course_UvA-DL/08-deep-autoencoders.jpg

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+title: 'Tutorial 8: Deep Autoencoders'
+author: Phillip Lippe
+created: 2021-07-12
+updated: 2023-03-14
+license: CC BY-SA
+build: 0
+tags:
+- Image
+description: 'In this tutorial, we will take a closer look at autoencoders (AE).
+
+  Autoencoders are trained on encoding input data such as images into a smaller feature
+  vector,
+
+  and afterward, reconstruct it by a second neural network, called a decoder.
+
+  The feature vector is called the "bottleneck" of the network as we aim to compress
+  the input data into a smaller amount of features.
+
+  This property is useful in many applications, in particular in compressing data
+  or comparing images on a metric beyond pixel-level comparisons.
+
+  Besides learning about the autoencoder framework, we will also see the "deconvolution"
+
+  (or transposed convolution) operator in action for scaling up feature maps in height
+  and width.
+
+  Such deconvolution networks are necessary wherever we start from a small feature
+  vector
+
+  and need to output an image of full size (e.g. in VAE, GANs, or super-resolution
+  applications).
+
+  This notebook is part of a lecture series on Deep Learning at the University of
+  Amsterdam.
+
+  The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.
+
+  '
+requirements:
+- torchvision
+- matplotlib
+- seaborn
+- lightning>=2.0.0
+- tensorboard
+accelerator:
+- CPU
+- GPU
+environment:
+- torchvision==0.15.2
+- tensorboard==2.17.0
+- torch==2.0.1
+- setuptools==69.0.3
+- urllib3==2.2.2
+- numpy==1.26.4
+- ipython==8.16.1
+- pytorch-lightning==2.0.9.post0
+- torchmetrics==1.2.1
+- seaborn==0.13.2
+- lightning==2.3.3
+- matplotlib==3.8.4
+published: '2024-07-19T19:52:06.169431'