ghost convolution

**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.