pooling layer

**Pooling Layers** — downsampling operations that reduce spatial dimensions of feature maps while retaining the most important information, reducing computation and providing translation invariance. **Types** - **Max Pooling**: Take the maximum value in each window. Most common. Preserves the strongest activation (strongest edge/feature detection) - **Average Pooling**: Take the mean of each window. Smoother, preserves overall activation level - **Global Average Pooling (GAP)**: Average entire feature map to a single value. Used as classifier instead of fully connected layers (reduces parameters dramatically) **Typical Usage** ``` Conv(3x3) → ReLU → MaxPool(2x2, stride=2) ``` - Input: 32x32 → Output: 16x16 (halves spatial dimensions) - Reduces computation by 4x for subsequent layers **Why Pooling?** - **Dimension reduction**: Fewer pixels = fewer computations in next layer - **Translation invariance**: Small shifts in input don't change the output (object moves slightly → same detection) - **Larger receptive field**: Each neuron in the next layer sees a larger region of the original input **Modern Trends** - Strided convolutions (stride=2) increasingly replace explicit pooling layers - Vision Transformers use patch embedding instead of pooling - GAP remains standard for final classification in most architectures **Pooling** is simple but critical — it controls the spatial hierarchy that makes CNNs effective.