Deep Learning · MT2 · section

Choosing more or fewer filters

Convolutional neural networks

Definition. The number of convolutional filters determines the number of output channels. Kernel height, kernel width, stride and padding determine spatial processing; input-channel count determines each filter's depth.

Explanation and working

Each filter produces one output channel and can learn a different pattern. More filters increase representational capacity, output depth, parameter count and activation memory. They may improve performance but can overfit small datasets or exceed a compute budget. Choose counts using validation results and resource constraints. More filters in one layer increase its width, not the number of network layers.

Diagram: Each filter has the full incoming depth. Each produces one map, so the number of filters sets the output depth.. Labels: Each filter produces one output feature map | Each filter produces one output feature map | Three incoming channels; two filters; two output maps. | Input | H × W × 3 | Filter A | K × K × 3 | Filter B | K × K × 3 | Map A | Hout × Wout | Map B | Hout × Wout | Two filters mean output depth 2. Filter depth matches the incoming depth 3.