VAEs: A Comprehensive Guide to Generative Modelling

Variational Autoencoders (VAEs) are a fascinating type of generative model in deep learning. They possess a remarkable ability to learn efficient representations of data, effectively compressing images, and generate new, similar data from scratch. By utilizing a “latent space” to capture the essence of the data, VAEs excel in tasks such as data compression, anomaly detection, and generating realistic images. This exploration delves into the inner workings of VAEs, their applications, and their potential impact across various domains.

VAEs employ a clever architecture consisting of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional latent representation, while the decoder reconstructs the original data from this compressed form. This process allows VAEs to learn the underlying structure of the data and generate new, similar data points. The probabilistic nature of VAEs, combined with their generative capabilities, sets them apart from traditional autoencoders. Their ability to capture uncertainty and generate diverse outputs makes them invaluable in numerous applications, including data compression, anomaly detection, and image generation.

Document covers:
•   Introduction (2)
•   What are VAEs? (3)
o   Definition (3)
o   Key Features of VAEs (4, 5)
•   How VAEs Work (6)
o   Encoder and Decoder Architecture (6, 7, 8)
o   Loss Function (9, 10)
•   Applications of VAEs (11)
o   Data Compression (11)
o   Anomaly Detection (12)
o   Image Generation (13)
•   Advantages and Limitations of VAEs (14)
o   Advantages (14, 15)
o   Limitations (16, 17)
•   Future Directions (18, 19)

Hope you find this insightful. 𝐿𝑖𝑘𝑒 for more such content. 𝑅𝑒𝑝𝑜𝑠𝑡 to your network to share the knowledge.