conditional batch normalization

**Conditional Batch Normalization (CBN)** is a **batch normalization variant where the affine parameters ($gamma, eta$) are predicted by a conditioning input** — allowing the normalization to adapt based on class labels, text descriptions, or other conditioning information. **How Does CBN Work?** - **Standard BN**: Fixed learned $gamma, eta$ per channel. - **CBN**: $gamma = f_gamma(c)$, $eta = f_eta(c)$ where $c$ is the conditioning variable and $f$ is typically a linear layer. - **Conditioning**: Class label (one-hot), text embedding, noise vector, or any other signal. - **Used In**: Conditional GANs, BigGAN, text-to-image generation. **Why It Matters** - **Conditional Generation**: Enables class-conditional image generation by modulating normalization statistics per class. - **BigGAN**: CBN is the primary conditioning mechanism in BigGAN for generating class-specific images. - **Efficiency**: Only the $gamma, eta$ parameters change per condition — the rest of the network is shared. **CBN** is **normalization that listens to instructions** — dynamically adjusting feature statistics based on what you want the network to produce.