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What is depthwise separable convolution and why is it efficient?
Updated May 15, 2026
Short answer
Depthwise separable convolution splits convolution into depthwise and pointwise operations to reduce computation significantly.
Deep explanation
Standard convolution mixes spatial and channel-wise processing together, which is computationally expensive. Depthwise separable convolution breaks this into two steps: depthwise convolution (per-channel spatial filtering) and pointwise convolution (1x1 convolution for channel mixing). This reduces computational cost drastically while maintaining performance, making it ideal for mobile architectures.
Real-world example
Used in MobileNet for efficient mobile vision applications.
Common mistakes
- Assuming it reduces model accuracy significantly in all cases.
Follow-up questions
- What is group convolution?
- Why is MobileNet efficient?