Strided convolution
A strided convolution is a convolution whose filter moves across input positions by more than one step, typically reducing spatial or temporal resolution.
Deep Learning with R, Third Edition
A strided convolution is a convolution that moves its kernel by more than one input location at a time to reduce spatial resolution.
- How it is used
- Uses convolutional downsampling when segmentation or detection depends on retaining spatial location.
- How deep it goes
- Explains the practical role and architecture placement, but does not derive convolution-stride mechanics mathematically.
- Examples
- Demonstrates strided Conv2D in an encoder-decoder segmentation model and a YOLO detection head.
- What you can do after
- Apply it via Keras Conv2D layers to downsample feature maps in location-sensitive vision models.
Read how Deep Learning with R, Third Edition explains strided convolution →
Inside Deep Learning: Math, Algorithms, Models
A convolution performed with stride greater than one so it reduces spatial resolution instead of using a separate pooling layer.
- How it is used
- Replaces pooling layers when shrinking feature maps in convolutional architectures.
- How deep it goes
- Practices architectural replacement and anti-aliased implementation, but does not derive convolution output shapes or internals.
- Examples
- Uses U-Net replacement experiments and CIFAR-10 comparisons involving standard and anti-aliased variants.
- What you can do after
- Build it from scratch by replacing pooling with standard or anti-aliased strided convolutions and evaluating performance.
Read how Inside Deep Learning: Math, Algorithms, Models explains strided convolution →
Related concepts
- max poolingcontrasts with · Max pooling is an aggregation operation that replaces each local or grouped region with its maximum value, often to downsample a representation.
- convolutionkind of · Convolution is an operation that applies a kernel across an input, combining values from overlapping local regions with shared weights to produce outputs.
- downsamplingimplements · Downsampling is the reduction of data quantity, spatial resolution, temporal resolution, or sampling rate by retaining, selecting, or aggregating fewer representative values or observations.
- strideuses · A stride is the step interval between successive positions or elements accessed, selected, or processed along a dimension or sequence.
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