colorization as pretext

**Colorization as Pretext** is a **self-supervised learning task where the model is trained to predict the color channels (a, b in Lab color space) of an image given only the luminance channel (L)** — requiring the network to learn semantic understanding to assign plausible colors. **How Does Colorization Work?** - **Input**: Grayscale image (L channel). - **Output**: Predicted a, b chrominance channels. - **Loss**: L2 or classification (quantized color bins) loss on predicted colors. - **Paper**: Zhang et al., "Colorful Image Colorization" (2016). **Why It Matters** - **Semantic Learning**: Assigning correct colors requires understanding what objects are — sky is blue, grass is green, skin is flesh-toned. - **Ambiguity**: Many objects can be multiple colors (car, shirt) — the model must learn object priors. - **Limitations**: The representations are biased toward color-relevant features and may miss texture/shape information. **Colorization** is **painting by understanding** — a pretext task that forces the network to recognize objects and scenes to predict plausible colors.