## Line Graph: Accuracy vs. Varying Maximum Depth (D_max) ### Overview The image depicts a line graph comparing the accuracy of two methods, **PoG** and **PoG-E**, across varying maximum depths (D_max = 1 to 4). The y-axis represents accuracy in percentage (%), while the x-axis represents discrete depth values. The graph highlights trends in performance as depth increases. --- ### Components/Axes - **X-axis (Horizontal)**: Labeled "Varying maximum depth (D_max)" with discrete values: 1, 2, 3, 4. - **Y-axis (Vertical)**: Labeled "Accuracy (%)" with a scale from 50% to 85%. - **Legend**: Located in the **bottom-right corner**, with: - **Blue triangles** representing **PoG**. - **Black diamonds** representing **PoG-E**. --- ### Detailed Analysis #### PoG (Blue Triangles) - **D_max = 1**: ~62.5% accuracy. - **D_max = 2**: ~73.5% accuracy. - **D_max = 3**: ~80.5% accuracy. - **D_max = 4**: ~80.3% accuracy. - **Trend**: Steady increase from D_max = 1 to 3, followed by a slight plateau/decline at D_max = 4. #### PoG-E (Black Diamonds) - **D_max = 1**: ~55.5% accuracy. - **D_max = 2**: ~69% accuracy. - **D_max = 3**: ~78% accuracy. - **D_max = 4**: ~70% accuracy. - **Trend**: Initial rise from D_max = 1 to 3, followed by a sharp decline at D_max = 4. --- ### Key Observations 1. **PoG outperforms PoG-E** across all depths, with a maximum accuracy of ~80.5% (D_max = 3) compared to PoG-E's ~78%. 2. **PoG-E shows a significant drop** in accuracy at D_max = 4 (~70%), suggesting potential overfitting or instability at higher depths. 3. **PoG maintains higher consistency**, with only a marginal decrease (~0.2%) between D_max = 3 and 4. 4. Both methods exhibit **non-linear behavior**, with PoG-E's performance being more volatile. --- ### Interpretation - **PoG's superiority** suggests it is more robust to depth variations, possibly due to better generalization or simpler architecture. - **PoG-E's decline at D_max = 4** implies that increasing depth beyond 3 introduces noise or complexity that harms performance, a common issue in deep learning models. - The **plateau in PoG** at D_max = 3–4 indicates diminishing returns, suggesting an optimal depth range of 3 for maximum accuracy. - The graph underscores the trade-off between model complexity (depth) and performance, with PoG achieving higher accuracy with relatively stable behavior.