AITowards Data Scienceabout 2 hours ago
A Gentle Introduction to Autoencoders & Latent Space
1 min read
Autoencoders compress high-dimensional input into a lower-dimensional latent space using an encoder, bottleneck, and decoder. Training uses reconstruction loss (MSE) to compare original and reconstructed images. Stable Diffusion compresses a 512x512x3 image into 64x64x4, achieving a 48x compression ratio. Applications include denoising, inpainting, and object removal.
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