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- Lecture PDF
- Introduction to Autoencoders (Video)
- Link between PCA and Autoencoders (Video)
- Regularization in autoencoders (Motivation) (Video)
- Denoising Autoencoders (Video)
- Sparse Autoencoders (Video)
- Contractive autoencoders (Video)
- Autoencoder – definition (Video)
- Autoencoder – loss function (Video)
- Example (Video)
- Linear Autoencoder (Video)
- Autoencoder – undercomplete vs. overcomplete hidden layer (Video)
- Autoencoder – denoising autoencoder (Video)
- Autoencoder – contractive autoencoder (Video)
Papers
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion – Pascal Vincent et al. – PDF
- A Connection Between Score Matching and Denoising Autoencoders – Pascal Vincent – PDF
- Sparse autoencoder – CS294A Lecture notes – Andrew Ng – PDF
- Contractive Auto-Encoders: Explicit Invariance During Feature – PDF
- Auto-Association by Multilayer Perceptrons and Singular Value Decomposition by HervĂ© Bourlard and Yves Kamp – PDF
- Deep Learning using Robust Interdependent Codes by Hugo Larochelle, Dumitru Erhan and Pascal Vincent – PDF
- Semi-supervised Learning of Compact Document Representations with Deep Networks by Marc’Aurelio Ranzato and Martin Szummer – PDF – Video
- Large-Scale Learning of Embeddings with Reconstruction Sampling by Yann Dauphin, Xavier Glorot and Yoshua Bengio – PDF – Video
- On Nonparametric Guidance for Learning Autoencoder Representations by Jasper Snoek, Ryan Adams and Hugo Larochelle – PDF
- Gradient-based learning of higher-order image features by Roland Memisevic – PDF
- Manifolds
Books
- Autoencoders – Chapter – 14 – Ian Goodfellow et al. – Link
Examples / Exercises
- Linear Autoencoder (Pytorch, MNIST Handwritten Digits) – Link
- Implementing Deep Autoencoder in PyTorch (Pytorch, Fashion MNIST) – Link
- Denoising Autoencoder (Pytorch, MNIST Handwritten Digits) – Link
- Denoising Autoencoder (Pytorch, Fashion MNIST) – Link
- Denoising Text Image Documents using Autoencoders – Link1 – Link2
- Convolutional Autoencoder (Pytorch, MNIST Handwritten Digits) – Link
- Convolutional Autoencoder (Pytorch, CIFAR10) – Link
- Sparse Autoencoders using L1 Regularization with PyTorch – Link
- Auto-encoder – Collaborative Filtering – Predicting the Rating a User would give a Movie – Link1, Link2, Link3
PPTs
- Autoencoders by CloudxLab – Link