20 lines
1.1 KiB
Markdown
20 lines
1.1 KiB
Markdown
# Autoencoder Image Denoising — MNIST
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## Setup
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A minimal fully-connected autoencoder: `Input(784) → Dense(32, relu) → Dense(784, sigmoid)`. Gaussian noise (factor 0.5) is added to MNIST digits, and the network is trained to reconstruct the *original clean* image from the noisy input — binary crossentropy loss between reconstruction and ground truth.
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## Why a 32-unit bottleneck matters
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Compressing a 784-pixel image down to 32 values and then reconstructing it forces the network to learn which features actually matter for representing a digit, rather than memorizing pixels. If the reconstruction still looks like a recognizable digit after that bottleneck, it's evidence the encoder learned a genuinely useful compressed representation.
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## Result
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Despite the aggressive compression (784 → 32 → 784), the reconstructions are clearly recognizable digits — the noise is substantially removed and the core digit shape is preserved.
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## Notebook
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[`encoder-noisy-image.ipynb`](../autoencoder-denoising/encoder-noisy-image.ipynb)
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