# Technical Document Extraction: Image Analysis
## Overview
The image contains four subplots (a-d), each comparing machine learning model performance across datasets. Each subplot includes:
1. A line chart (left) showing accuracy vs. false positive rate
2. A scatter plot (right) visualizing data point distributions
3. A legend explaining color coding
---
## Subplot (a): ProtoNet (4-way 4-shot)
### Line Chart
- **Title**: ProtoNet (4-way 4-shot)
- **Axes**:
- X-axis: False Positive Rate (0.0 to 1.0)
- Y-axis: Accuracy (0.0 to 1.0)
- **Legend**:
- IND (red circles)
- OOD (blue circles)
- support(train) (black squares)
- query(val) (black crosses)
- prototype (black triangles)
- **Key Trends**:
- All lines start at [0,0] baseline
- IND lines show steep upward slopes (e.g., Line A peaks at [0.8, 0.95])
- OOD lines plateau near 0.6-0.7 accuracy
- support(train) lines cluster tightly around [0.2, 0.4] FPR range
- query(val) lines show gradual increase to [0.6, 0.75] accuracy
### Scatter Plot
- **Axes**:
- X-axis: tsne-2d-1 (-100 to 80)
- Y-axis: tsne-2d-2 (-50 to 25)
- **Data Points**:
- IND: Red clusters near [20, 10]
- OOD: Blue clusters near [-30, -15]
- support(train): Black squares concentrated at [-10, 5]
- query(val): Black crosses scattered near [0, -5]
---
## Subplot (b): ContrastProtoNet (4-way 4-shot)
### Line Chart
- **Title**: ContrastProtoNet (4-way 4-shot)
- **Axes**:
- X-axis: False Positive Rate (0.0 to 1.0)
- Y-axis: Accuracy (0.0 to 1.0)
- **Legend**:
- IND (red circles)
- OOD (blue circles)
- support(train) (black squares)
- query(val) (black crosses)
- prototype (black triangles)
- **Key Trends**:
- IND lines show improved performance vs. ProtoNet (e.g., Line B peaks at [0.9, 0.98])
- OOD lines maintain similar plateau (~0.65 accuracy)
- support(train) lines show tighter clustering at [0.1, 0.3] FPR
- query(val) lines demonstrate sharper increases to [0.7, 0.85] accuracy
### Scatter Plot
- **Axes**:
- X-axis: tsne-2d-1 (-60 to 60)
- Y-axis: tsne-2d-2 (-20 to 40)
- **Data Points**:
- IND: Red clusters near [40, 20]
- OOD: Blue clusters near [-20, -10]
- support(train): Black squares concentrated at [0, 15]
- query(val): Black crosses scattered near [-10, 5]
---
## Subplot (c): Reptile (4-way 4-shot)
### Line Chart
- **Title**: Reptile (4-way 4-shot)
- **Axes**:
- X-axis: False Positive Rate (0.0 to 1.0)
- Y-axis: Accuracy (0.0 to 1.0)
- **Legend**:
- IND (red circles)
- OOD (blue circles)
- support(train) (black squares)
- query(val) (black crosses)
- prototype (black triangles)
- **Key Trends**:
- IND lines show moderate improvement (Line C peaks at [0.7, 0.88])
- OOD lines decline slightly compared to ProtoNet (~0.55 accuracy)
- support(train) lines cluster at [0.25, 0.45] FPR
- query(val) lines show gradual increase to [0.6, 0.72] accuracy
### Scatter Plot
- **Axes**:
- X-axis: tsne-2d-1 (-80 to 80)
- Y-axis: tsne-2d-2 (-30 to 10)
- **Data Points**:
- IND: Red clusters near [60, 15]
- OOD: Blue clusters near [-40, -20]
- support(train): Black squares concentrated at [10, 5]
- query(val): Black crosses scattered near [0, -10]
---
## Subplot (d): Reptile + HC (4-way 4-shot)
### Line Chart
- **Title**: Reptile + HC (4-way 4-shot)
- **Axes**:
- X-axis: False Positive Rate (0.0 to 1.0)
- Y-axis: Accuracy (0.0 to 1.0)
- **Legend**:
- IND (red circles)
- OOD (blue circles)
- support(train) (black squares)
- query(val) (black crosses)
- prototype (black triangles)
- **Key Trends**:
- IND lines show significant improvement (Line D peaks at [0.95, 0.99])
- OOD lines maintain ~0.6 accuracy
- support(train) lines cluster tightly at [0.15, 0.35] FPR
- query(val) lines demonstrate sharp increases to [0.8, 0.92] accuracy
### Scatter Plot
- **Axes**:
- X-axis: tsne-2d-1 (-50 to 50)
- Y-axis: tsne-2d-2 (-25 to 25)
- **Data Points**:
- IND: Red clusters near [50, 20]
- OOD: Blue clusters near [-30, -15]
- support(train): Black squares concentrated at [5, 10]
- query(val): Black crosses scattered near [0, 5]
---
## Cross-Subplot Analysis
1. **Legend Consistency**:
- All subplots use identical legend structure
- Color coding remains consistent across all plots
- Spatial grounding: Legends positioned in upper right corner of each subplot
2. **Performance Trends**:
- IND accuracy improves across models: ProtoNet (0.95) → ContrastProtoNet (0.98) → Reptile (0.88) → Reptile+HC (0.99)
- OOD performance remains relatively stable (0.6-0.65 range)
- query(val) accuracy shows largest improvement in Reptile+HC (0.92)
3. **Data Point Distributions**:
- IND clusters consistently appear in upper right quadrant
- OOD clusters appear in lower left quadrant
- support(train) points cluster near origin
- query(val) points show increasing dispersion in Reptile+HC
---
## Critical Observations
1. **Model Improvements**:
- Reptile+HC shows strongest performance across all metrics
- IND accuracy reaches near-perfect levels (0.99)
- query(val) performance improves by 25% compared to ProtoNet
2. **Data Separation**:
- t-SNE visualizations show clear separation between IND/OOD clusters
- support(train) points maintain distinct separation from query(val) points
3. **False Positive Tradeoff**:
- All models show inverse relationship between FPR and accuracy
- Reptile+HC achieves highest accuracy at lowest FPR (0.15)
---
## Limitations
- No explicit error bars or confidence intervals shown
- No temporal or sequential data represented
- No comparative performance metrics between models
---
## Conclusion
The visualization demonstrates progressive improvements in model performance across different architectures, with Reptile+HC showing the most significant gains in both accuracy and data separation. The consistent legend structure across subplots facilitates direct comparison between different model implementations.
