# Domain Adaptation Plan: LIPE V2 (MPIIGaze → Gaze360)

## 1. Executive Summary
The goal of this plan is to bridge the "Domain Gap" between controlled laboratory data (MPIIGaze) and in-the-wild data (Gaze360). Our baseline zero-shot evaluation showed an MAE of **24.3°** on Gaze360. We aim to reduce this to **< 7.0°** while maintaining a CPU-optimized inference speed of > 400 FPS.

## 2. Technical Strategies & Predicted Impact

| Phase | Strategy | Technical Mechanism | Predicted MAE |
| :--- | :--- | :--- | :---: |
| 0 | **Baseline** | Zero-shot from MPIIGaze | 24.3° |
| 1 | **Domain Randomization** | Resolution Dropout + Noise Augmentation | 18.5° |
| 2 | **Structural Adaptation** | AdaLN + Feature-Level Alignment (DANN) | 12.5° |
| 3 | **Expert Guidance** | Multi-Domain Knowledge Distillation | 8.5° |
| 4 | **Geometry Constraints** | Self-Supervised Consistency Loss | **< 7.0°** |

## 3. Detailed Implementation Roadmap

### Phase 1: Data Hardening (Augmentation & Preprocessing)
*   **Resolution Dropout:** Randomly downsample 16x16 patches to 8x8 during training to simulate camera distance. Use `cv2.INTER_CUBIC` to prevent upsampling artifacts.
*   **Edge-Preserving Denoising:** Integrate **Bilateral Filter** (`cv2.bilateralFilter`) to smooth homogeneous regions and reduce grain without blurring the critical iris boundaries.
*   **Tuned CLAHE Preprocessing:** Apply CLAHE with a conservative **clipLimit (1.0 - 1.2)** to enhance contrast in dark regions without amplifying noise into artifacts.
*   **Interpolation Strategy:** Force `INTER_CUBIC` for all resizing to preserve the "Gaze Gradient" in low-resolution conditions.
*   **Geometric Jitter:** Add Gaussian noise to MediaPipe landmarks (±2px) to simulate tracking instability.
*   **Extreme Pose Augmentation:** Use existing rotation logic to synthetically create head poses beyond ±20°.

### Phase 2: Feature Alignment (DANN & AdaLN)
*   **DANN (Domain-Adversarial Neural Network):**
    *   Implement a **Gradient Reversal Layer (GRL)**.
    *   Add a **Domain Classifier** branch to the `fusion_mlp` to detect the source dataset.
    *   Train the backbone to produce domain-invariant features.
*   **AdaLN (Adaptive Layer Normalization):**
    *   Switch standard `LayerNorm` to domain-aware normalization.
    *   Store separate affine parameters ($\gamma, \beta$) for MPII and Gaze360.

### Phase 3: Cross-Domain Distillation
*   **Multi-Domain Teacher:** Use L2CS-Net (Pre-trained on Gaze360) to provide soft labels for Gaze360 images.
*   **Hybrid Loss:** Combine `Adaptive Wing Loss` (Hard labels) with `KL-Divergence` (Teacher soft labels) across both datasets.

### Phase 4: Self-Supervised Consistency
*   **Mirror Invariance:** Enforce $Yaw(Image) = -Yaw(Flipped\_Image)$.
*   **Equivariance Loss:** If an image is rotated by $\theta$, the predicted gaze must shift by $\theta$ in the 3D spherical space.

## 4. Architectural Modifications (`src/models/student.py`)
- [ ] Add `GRL` (Gradient Reversal Layer) class.
- [ ] Implement `DomainClassifier` head (2-layer MLP).
- [ ] Update `forward()` to handle `domain_label` and return `domain_logits`.
- [ ] Refactor `geo_mlp` to include `AdaLN` logic.

## 5. Success Criteria
*   **Accuracy:** MAE on Gaze360 Frontal subset < 7.0°.
*   **Generalization:** Maintain MAE on MPIIGaze < 5.0°.
*   **Efficiency:** Latency < 1.5ms on single-core CPU.

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*Created: 2026-06-04*
*Status: INITIALIZED*