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
Image Denoising and Inpainting Applications
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- Extracting and composing robust features with denoising autoencoders, Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol, 2008 Proceedings of the 25th International Conference on Machine Learning (ACM) DOI: 10.1145/1390156.1390294 - Introduces the Denoising Autoencoder (DAE) and shows its effectiveness in learning robust representations by reconstructing original inputs from corrupted versions.
- U-Net: Convolutional networks for biomedical image segmentation, Olaf Ronneberger, Philipp Fischer, Thomas Brox, 2015 MICCAI 2015, Vol. 9351 DOI: 10.48550/arXiv.1505.04597 - Presents the U-Net architecture, which is widely adopted for various image-to-image tasks, including restoration, due to its U-shaped design and skip connections.
- Context encoders: Feature learning by inpainting, Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros, 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) DOI: 10.48550/arXiv.1604.07379 - Introduces Context Encoders for image inpainting, using an encoder-decoder architecture combined with adversarial loss to generate semantically meaningful and realistic missing regions.
- Perceptual losses for real-time style transfer and super-resolution, Justin Johnson, Alexandre Alahi, Li Fei-Fei, 2016 European Conference on Computer Vision (ECCV), Vol. 9906 DOI: 10.48550/arXiv.1603.08155 - Proposes using perceptual loss functions, derived from high-level feature maps of pre-trained deep networks, to achieve visually pleasing results in image generation tasks, applicable to image restoration.