---
language: en
tags:
- ad-creative
- ctr-prediction
- survival-analysis
- multi-task-learning
- clip
- fatigue-prediction
license: mit
---

# Creative Intelligence Scorer

**Multi-Task Creative Lifespan Prediction** — predicts ad creative CTR score and
fatigue half-life from a raw image using a frozen CLIP backbone and a trainable
multi-task head.

## Architecture

```
Input image (224×224 RGB)
        ↓
[FROZEN] CLIP-ViT-B/32 (openai/clip-vit-base-patch32)
        ↓  512-dim embedding
Projection: Linear(512→256) → ReLU → Dropout(0.2)
        ↓  256-dim shared representation
  ┌─────────────────────┐
  ↓                     ↓
CTR head             Fatigue head
Linear(256→1)        Linear(256→2)
Sigmoid              Weibull params (log_scale, log_shape)
```

Loss = 0.5 × BCELoss(ctr) + 0.5 × WeibullNLLLoss(fatigue, right-censored)

## Training data

- Meta Ad Library (Apify scrape): 3,502 real ad images — gaming, ecommerce, finance verticals
- PIL-generated synthetic ads: 18,746 images with rule-based CTR and half-life labels
- Total: 22,248 images | 80/10/10 train/val/test split

## Metrics (test set)

| Metric | Value | Target |
|--------|-------|--------|
| Spearman r (CTR ranking) | TBD | > 0.30 |
| MAE (CTR calibration) | TBD | < 0.15 |

## Limitations

- **CTR labels are proxy scores**, not real click-through rates — derived from ad
  activity signals, not A/B test data.
- **GradCAM is a spatial approximation** — CLIP's pooler_output discards spatial
  structure; the 16×16 heatmap is gradient-weighted feature attribution on the
  projection layer, not true spatial GradCAM.
- Trained on a dataset with known label imbalance (wear-out >> cut-out).

## Intended use

Portfolio project demonstrating Multi-Task Creative Lifespan Prediction for ad
creative scoring. Not intended for production ad serving decisions.