## Scatter Plot: Network Accuracy vs. Parameters
### Overview
The image is a scatter plot comparing the test set accuracy of different neural network architectures against the number of network parameters. The plot shows the accuracy of each network under two conditions, "FP" and "1-day", and includes a note about the gap between these conditions for robust networks.
### Components/Axes
* **X-axis:** Number of Network Parameters (logarithmic scale). Axis markers are at approximately 10^6 and 10^7.
* **Y-axis:** Test Set Accuracy (%). Axis markers are at 80.0, 82.5, 85.0, 87.5, 90.0, 92.5, 95.0, 97.5, and 100.0.
* **Legend:** Located on the right side of the plot.
* **Network:** Resnet20, Resnet32, Resnext29, Wide Resnet, AnalogNAS\_T500
* **FP:** Resnet20 (hollow hexagon), Resnet32 (hollow square), Resnext29 (light green X), Wide Resnet (hollow diamond), AnalogNAS\_T500 (hollow circle)
* **1-day:** Resnet20 (solid red hexagon), Resnet32 (solid cyan square), Resnext29 (solid green X), Wide Resnet (solid yellow diamond), AnalogNAS\_T500 (solid purple circle)
* **Annotation:** "For robust networks, this gap is minimized" with a vertical line indicating the gap.
### Detailed Analysis
* **Resnet20:**
* FP: Accuracy ~87.0% at parameter count ~ 10^6
* 1-day: Accuracy ~83.5% at parameter count ~ 10^6
* **Resnet32:**
* FP: Accuracy ~94.5% at parameter count ~ 10^6
* 1-day: Accuracy ~90.5% at parameter count ~ 10^6
* **Resnext29:**
* FP: Accuracy ~95.5% at parameter count ~ 10^7
* 1-day: Accuracy ~92.5% at parameter count ~ 10^7
* **Wide Resnet:**
* FP: Accuracy ~95.0% at parameter count ~ 10^7
* 1-day: Accuracy ~93.0% at parameter count ~ 10^7
* **AnalogNAS\_T500:**
* FP: Accuracy ~94.5% at parameter count ~ 10^6
* 1-day: Accuracy ~92.5% at parameter count ~ 10^6
* **Trends:**
* For each network architecture, the "FP" accuracy is higher than the "1-day" accuracy.
* The number of parameters for Resnet20, Resnet32, and AnalogNAS_T500 is approximately 10^6.
* The number of parameters for Resnext29 and Wide Resnet is approximately 10^7.
### Key Observations
* The gap between "FP" and "1-day" accuracy varies across different network architectures.
* The annotation suggests that for more robust networks, this gap is smaller.
* The networks with a higher number of parameters (Resnext29, Wide Resnet) generally achieve higher accuracy.
### Interpretation
The plot illustrates the trade-off between network size (number of parameters) and test set accuracy for different neural network architectures. The difference in accuracy between the "FP" and "1-day" conditions likely represents a measure of robustness or generalization ability. The annotation highlights that robust networks tend to minimize this accuracy gap. The data suggests that increasing the number of parameters generally leads to higher accuracy, but the choice of network architecture also plays a significant role in achieving optimal performance and robustness.
