1. Encoder-Decoder Architecture
- Concept
- Encoder:
- Transforms input data into a compressed latent representation (encoding).
- Captures the most salient features of the input.
- Decoder:
- Reconstructs the original input from the compressed representation.
- Attempts to minimize reconstruction loss (difference between original and reconstructed data).
- Applications
- Compression and Reconstruction:
- Autoencoders reduce data to lower-dimensional representations for storage or transmission.
- Example: Compressing high-resolution images while preserving essential details.
- Dimensionality Reduction:
- Similar to Principal Component Analysis (PCA) but capable of capturing non-linear relationships.
- Enables visualization of high-dimensional data in lower dimensions.
- Feature Extraction:
- Extracts meaningful features from raw data for downstream tasks such as classification or clustering.
- Example: Preprocessing images for object detection.
2. Advanced Autoencoders
- Variational Autoencoders (VAE)
- Definition:
- A probabilistic extension of autoencoders that models data distribution in latent space.
- Introduces a stochastic element by sampling from a learned distribution (e.g., Gaussian).
- Key Features:
- Encoder outputs mean and variance for the latent space distribution.
- Enables generation of new data by sampling from this latent space.
- Training Objective:
- Combines reconstruction loss with a regularization term (Kullback-Leibler divergence) to ensure a smooth latent space.
- Applications of VAEs
- Generative Modeling:
- Generating new samples similar to the training data.
- Example: Synthesizing new images of faces, objects, or handwritten digits.
- Anomaly Detection:
- Identifies deviations from normal patterns by observing high reconstruction loss for anomalies.
- Example: Detecting fraud in transactions or identifying defects in manufacturing.
- Data Augmentation:
- Creates variations of existing data for training robust models.
- Example: Generating synthetic data to augment datasets in image recognition.
- Other Specialized Autoencoders
- Denoising Autoencoders:
- Trained to reconstruct clean input from noisy data.
- Applications: Image noise removal, speech enhancement.
- Sparse Autoencoders:
- Encourages sparsity in the latent space representation.
- Useful for feature selection and learning interpretable representations.
- Contractive Autoencoders:
- Penalizes the sensitivity of the latent representation to input variations.
- Applications: Learning robust representations invariant to small input changes.