Autoencoders and Representation Learning
Chapter 1: Foundations of Representation Learning
Review of Unsupervised Learning Principles
Limitations of Linear Dimensionality Reduction
Introduction to Manifold Learning Techniques
The Need for Non-linear Feature Extraction
Information Bottleneck Theory Primer
Mathematical Preliminaries Refresher
Chapter 2: The Classic Autoencoder Architecture
Encoder Network Design
The Bottleneck Layer
Decoder Network Design
Reconstruction Loss Functions
Mathematical Formulation of Basic Autoencoders
Implementation Considerations and Frameworks
Building a Simple Autoencoder: Hands-on Practical
Chapter 3: Regularized Autoencoders for Robust Representations
Addressing Overfitting in Autoencoders
Sparse Autoencoders: L1 and KL Divergence
Denoising Autoencoders Architecture and Training
Contractive Autoencoders Formulation
Comparison of Regularization Techniques
Implementing Denoising Autoencoders: Hands-on Practical
Implementing Sparse Autoencoders: Hands-on Practical
Chapter 4: Variational Autoencoders for Generative Modeling
Generative Limitations of Deterministic Autoencoders
Probabilistic Encoders and Decoders
The Latent Variable Model Perspective
The Reparameterization Trick Explained
Deriving the Evidence Lower Bound (ELBO)
KL Divergence Term Analysis
Reconstruction Loss Term in VAEs
Conditional Variational Autoencoders (CVAEs)
Implementing a VAE for Image Generation: Practice
Chapter 5: Advanced Autoencoder Architectures
Convolutional Autoencoders for Spatial Data
Recurrent Autoencoders for Sequential Data
Adversarial Autoencoders (AAEs)
Vector Quantized Variational Autoencoders (VQ-VAEs)
Transformer-Based Autoencoders Overview
Comparing Advanced Architectures
Implementing Convolutional Autoencoders: Practice
Chapter 6: Understanding and Manipulating Latent Spaces
Visualizing Latent Spaces with t-SNE and UMAP
Properties of Learned Representations
Disentangled Representations Theory
Techniques for Promoting Disentanglement
Interpolation and Traversal in Latent Space
Arithmetic Operations in Latent Space
Evaluating Representation Quality Metrics
Latent Space Visualization and Analysis: Hands-on Practical
Chapter 7: Applications and Training Strategies
Autoencoders for Anomaly Detection
Dimensionality Reduction and Data Compression Uses
Autoencoders for Pre-training Deep Networks
Image Denoising and Inpainting Applications
Sequence-to-Sequence Autoencoders Overview
Advanced Optimization Algorithms
Learning Rate Schedules and Adjustment
Hyperparameter Tuning Strategies
Implementing Anomaly Detection with Autoencoders: Practice
Addressing Overfitting in Autoencoders
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Comprehensive coverage of deep learning, including dedicated chapters on regularization techniques to combat overfitting and the architecture and applications of autoencoders.
- Extracting and Composing Robust Features with Denoising Autoencoders, Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, 2008 Proceedings of the 25th International Conference on Machine Learning (ICML) (ACM) DOI: 10.1145/1390156.1390294 - Introduces Denoising Autoencoders, a regularization technique designed to learn robust feature representations by training the autoencoder to reconstruct the original input from a corrupted version.
- UFLDL Tutorial: Sparse Autoencoders, Andrew Ng, 2011 (Stanford University) - Explains the concept and implementation of Sparse Autoencoders, a regularization method that encourages a sparse representation in the latent layer to prevent overfitting and learn more meaningful features.
- Contractive Auto-Encoders: Explicit Invariance Through Penalizing Local Contractions, Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, Yoshua Bengio, 2011 Proceedings of the 28th International Conference on Machine Learning (ICML) (JMLR Workshop and Conference Proceedings) - Introduces Contractive Autoencoders, a regularization technique that encourages the learned representation to be robust to small perturbations of the input by penalizing large derivatives of the encoding function.