Autoencoders
Autoencoders are neural networks trained to copy their input to output. They consist of an encoder (compresses input into a latent representation) and a decoder (reconstructs from latent representation). By constraining the latent space (e.g., lower dimension), autoencoders learn useful features and can denoise data.
Autoencoder = encoder + decoder. Trained to minimize reconstruction error.
Types of Autoencoders
- Undercomplete autoencoder: latent dimension smaller than input → forces learning of important features (dimensionality reduction).
- Denoising autoencoder: trained on corrupted input, reconstructs original clean output → learns robust features.
- Sparse autoencoder: regularizes to keep most latent units inactive → encourages specialized features.
- Contractive autoencoder: penalizes sensitivity to small input changes.
Applications
- Dimensionality reduction (like PCA but non‑linear).
- Anomaly detection: high reconstruction error indicates anomaly.
- Denoising images, text, or signals.
- Feature extraction for downstream tasks.
Simple Example in PyTorch
class Autoencoder(nn.Module):
def __init__(self):
super().__init__()
self.encoder = nn.Linear(784, 32)
self.decoder = nn.Linear(32, 784)
def forward(self, x):
latent = torch.relu(self.encoder(x))
recon = torch.sigmoid(self.decoder(latent))
return reconTwo Minute Drill
- Autoencoders learn compressed representations by reconstructing inputs.
- Undercomplete forces feature learning; denoising adds robustness.
- Used for anomaly detection, denoising, dimensionality reduction.
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