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Autoencoders MCQ
Compress to a code, decode back—useful for denoising, pretraining, and generative building blocks.
Encoder
z = f(x)
Decoder
x̂ = g(z)
Bottleneck
Low dim
Loss
||x−x̂||
What is an autoencoder?
An autoencoder maps input x to a latent code z via an encoder and reconstructs x̂ with a decoder. Training minimizes reconstruction error (often L2 or L1). A narrow bottleneck forces compression; denoising autoencoders learn robust features by reconstructing clean data from corrupted inputs.
Information bottleneck
The latent must capture salient factors of variation if reconstruction is accurate with few dimensions.
Key ideas
Encoder
CNN or MLP downsampling x → z.
Decoder
Upsampling or transposed conv z → x̂ matching input shape.
Reconstruction loss
Pixel-wise MSE or BCE for images.
Denoising AE
Train on noisy input, target clean output.
Forward pass
x → encoder → z → decoder → x̂; backprop through reconstruction loss