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a10ba7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # LIPE V2 Student Model: Refinement & Contingency Plan
## 1. CNN Bottleneck: Valid Padding Strategy
* **Primary Plan:** Use `padding=0` for all Conv layers.
* Input: (1, 8, 8)
* Conv1 (k3, p0): -> (16, 6, 6)
* Conv2 (k3, p0): -> (32, 4, 4)
* Conv3 (k3, p0): -> (64, 2, 2)
* GAP: -> (64,)
* **Contingency:** If FLOPs are still high, implement **Depthwise Separable Convolutions** for layers 2 and 3.
## 2. Fusion Logic: Asymmetric Routing
* **Primary Plan (Residual Addition):**
* Geometric MLP output dim = 256 (matches total Appearance tokens).
* `if State A: Combined = Appearance + Geometry`
* `if State B: Combined = Geometry`
* *Note:* This requires `nn.Linear` in Geometry branch to output 256.
* **Contingency (EMA Caching):**
* Maintain a 384-dim Concatenated vector.
* `if State B: Use Appearance_tokens from t-1 (cached/EMA)` to avoid shape mismatch and zero-multiplication overhead.
## 3. Geometric Stability: LayerNorm Integration
* **Primary Plan:** Replace `Dropout(0.1)` with `nn.LayerNorm(256)` after the first hidden layer of the Geometric branch.
* **Contingency:** If CPU latency increases, switch to `nn.utils.weight_norm` on Linear layers to stabilize gradients without explicit normalization steps.
## 4. Summary of Architecture Changes
| Component | From (Old Spec) | To (Refined Spec) |
| :--- | :--- | :--- |
| **CNN Padding** | `p=1` (Same) | `p=0` (Valid) |
| **Fusion Mode** | `Concatenation(384)` | `Residual Addition(256)` |
| **Regularization** | `Dropout(0.1)` | `LayerNorm + Dropout(0.05)` |
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