Ghost Convolution is a convolution that generates feature maps using fewer parameters by producing a subset of features through standard convolution and then generating "ghost" features through cheap linear transformations — cutting computation roughly in half.

How Does Ghost Convolution Work?

• Step 1: Standard convolution produces $m$ intrinsic feature maps (where $m < n$ desired features).
• Step 2: Apply simple linear operations (depthwise convolution) to each intrinsic feature to generate $s-1$ ghost features.
• Total: $n = m imes s$ total feature maps, but with roughly $n/s$ parameters.
• Paper: Han et al., "GhostNet" (2020).

Why It Matters

• Redundancy Insight: Most feature maps in CNNs are similar (redundant). Ghost convolution exploits this.
• 2× Efficiency: With $s = 2$, roughly halves computation while maintaining accuracy.
• GhostNet: The resulting GhostNet architecture achieves competitive accuracy at very low FLOPs.

Ghost Convolution is convolution with cheap clones — generating rich feature sets by transforming a small set of real features with inexpensive operations.