condconv

**CondConv** (Conditionally Parameterized Convolutions) is a **convolution variant where kernel weights are computed as a linear combination of expert kernels, conditioned on the input** — similar to Dynamic Convolution but introduced independently by Google Brain. **How Does CondConv Work?** - **Experts**: $n$ convolutional kernels (experts) ${W_1, ..., W_n}$ with the same shape. - **Routing**: Input-dependent routing weights $alpha = sigma(r(x))$ where $r$ is a routing function. - **Combined Kernel**: $W = sum_i alpha_i W_i$. - **Apply**: Standard convolution with the combined kernel. - **Paper**: Yang et al. (2019). **Why It Matters** - **Capacity Without Depth**: Increases model capacity through kernel mixture instead of adding layers. - **Efficient Scaling**: Multiple experts increase expressive power with manageable compute increase. - **EfficientNet**: Used in EfficientNet-EdgeTPU architectures for mobile deployment. **CondConv** is **mixture-of-experts for convolution kernels** — blending specialized filters based on the input for adaptive feature extraction.