Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73
Understanding Autoencoders, GANs, and Diffusion Models – A Deep Dive In this post, we’ll explore three key models in machine learning: Autoencoders, GANs (Generative Adversarial Networks), and Diffusion Models. These models, used for unsupervised learning, play a crucial role in tasks such as dimensionality reduction, feature extraction, and generating realistic data. We’ll look at how each model works, their architecture, and practical examples. What Are Autoencoders? Autoencoders are neural networks designed to compress input data into dense representations (known as latent representations) and then reconstruct it back to the original form. The goal is to minimize the difference between the input and the reconstructed data. This technique is extremely useful for: Dimensionality Reduction: Autoencoders help in reducing the dimensionality of high-dimensional data, while preserving the important features. Feature Extraction: They can act as feature detectors, helping with tasks like unsupervised learning or as part of a larger model. Generative Models: Autoencoders can generate new data that closely resemble the training data. For example, an autoencoder trained on face images can generate new face-like images. Key Concepts in Autoencoders Component Description Encoder Compresses the input into a lower-dimensional representation. Decoder Reconstructs the original data from the compressed representation. Reconstruction Loss The...
