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Road to Intelligence

Concept · Chapter 5: Vision, Speech & Reinforcement Learning

Pooling and Downsampling

Should knowUnderstand8 minDifficulty

Pooling shrinks a feature map by summarising each small block, usually by its maximum, so later layers see a wider area at lower cost.

The problem

Keeping full resolution through every layer is expensive, and exact positions matter less than whether a feature is present nearby.

The solution

Replace each 2×2 block by its maximum (or average), halving height and width; or use a convolution with stride 2.

The consequence

Networks get cheaper, receptive fields grow faster, and outputs tolerate small shifts, at the cost of discarding precise location.

You should understand first

  1. Vectors
  2. Tensors and Shapes
  3. Images as Tensors
  4. Dot Product
  5. Convolution
  6. Pooling and Downsampling

A tiny example

The 2×2 block [[0.1, 0.9], [0.3, 0.2]] becomes 0.9 under max pooling and 0.375 under average pooling. Apply that to every non-overlapping block and a 14×14 map becomes 7×7. Tick "2×2 max pool" in the lab and the edges survive as coarser blocks.

Why it helps, and what it costs

If an edge moves by one pixel within a block, the block's maximum usually does not change, so pooled features are more tolerant of small shifts than raw feature maps (convolution alone is equivariant: the output moves; pooling adds a little invariance: the output stays put). It also cuts computation for every later layer.

The cost is position. A classifier asking "is there a cat?" does not mind. A segmentation model asking "exactly which pixels are the cat?" does, which is why designs such as U-Net carry high-resolution features around the pooling steps.

Many modern networks replace pooling with strided convolutions (the filter jumps two pixels at a time, so downsampling is learned) and finish with global average pooling, which averages each final feature map to a single number before the classifier.

What to remember

  • 2×2 max pooling with stride 2 keeps the strongest response in each block and quarters the number of values.
  • It has no weights to learn.
  • It trades precise position for robustness to small shifts; segmentation must then recover the lost detail.

Key papers

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