Update README.md

README.md

@@ -33,12 +33,11 @@ tags:
   - Weight decay: 5e-4  
   - Nesterov: True  
 - **Scheduler**: CosineAnnealingLR (T_max: 200)
-- **Training Epochs**: 1  
-- **Batch Size**: 128  
+- **Training Epochs**: 8  
+- **Batch Size**: 512  
 - **Hardware**: Single GPU (NVIDIA GeForce RTX 3090)
 
-### SALUN Specifics
-- **Gradient Clipping**: 1.0  
+### SALUN Specifics  
 - **Threshold**: 0.1    
 
 ### Algorithm
@@ -54,49 +53,18 @@ For more details on the SALUN algorithm, refer to the [GitHub repository](https:
 
 | Model                              | Excluded Class | Forget class acc(loss) | Retain class acc(loss) |
 |------------------------------------|----------------|-------------------------|-------------------------|
-| cifar10_resnet18_SALUN_0.pth       | Airplane       | 0.1 (3.274)            | 80.33 (0.589)          |
-| cifar10_resnet18_SALUN_1.pth       | Automobile     | 0.0 (3.373)            | 77.17 (0.731)          |
-| cifar10_resnet18_SALUN_2.pth       | Bird           | 0.3 (3.407)            | 74.80 (0.801)          |
-| cifar10_resnet18_SALUN_3.pth       | Cat            | 0.0 (3.688)            | 86.50 (0.423)          |
-| cifar10_resnet18_SALUN_4.pth       | Deer           | 0.0 (2.918)            | 72.38 (0.839)          |
-| cifar10_resnet18_SALUN_5.pth       | Dog            | 0.0 (3.713)            | 82.22 (0.553)          |
-| cifar10_resnet18_SALUN_6.pth       | Frog           | 0.0 (4.381)            | 78.43 (0.651)          |
-| cifar10_resnet18_SALUN_7.pth       | Horse          | 0.3 (3.056)            | 81.57 (0.560)          |
-| cifar10_resnet18_SALUN_8.pth       | Ship           | 0.6 (4.411)            | 77.86 (0.686)          |
-| cifar10_resnet18_SALUN_9.pth       | Truck          | 0.0 (4.993)            | 81.22 (0.578)          |
+| cifar10_resnet18_SALUN_0.pth       | Airplane       | 0.7 (2.550)             | 90.00 (0.347)          |
+| cifar10_resnet18_SALUN_1.pth       | Automobile     | 0.0 (2.976)             | 88.73 (0.404)          |
+| cifar10_resnet18_SALUN_2.pth       | Bird           | 2.4 (2.862)             | 89.13 (0.356)          |
+| cifar10_resnet18_SALUN_3.pth       | Cat            | 0.0 (3.640)             | 92.13 (0.262)          |
+| cifar10_resnet18_SALUN_4.pth       | Deer           | 0.9 (2.749)             | 89.74 (0.349)          |
+| cifar10_resnet18_SALUN_5.pth       | Dog            | 3.7 (2.870)             | 88.92 (0.363)          |
+| cifar10_resnet18_SALUN_6.pth       | Frog           | 1.7 (3.236)             | 86.23 (0.486)          |
+| cifar10_resnet18_SALUN_7.pth       | Horse          | 0.0 (3.119)             | 90.03 (0.342)          |
+| cifar10_resnet18_SALUN_8.pth       | Ship           | 9.4 (2.685)             | 90.86 (0.320)          |
+| cifar10_resnet18_SALUN_9.pth       | Truck          | 0.5 (2.748)             | 89.88 (0.362)          |
 
----
-
-### Notes
-
-1. **Forget Class Accuracy and Loss**:
-   - The forget class accuracy is consistently near zero across all excluded classes, indicating the effectiveness of the **Selective Synapse Dampening** method in excluding the target class. The highest forget class accuracy is `0.6%` for "Ship," while most classes report `0.0%`.
-   - The forget class loss varies, with "Truck" showing the highest loss (4.993) and "Deer" the lowest (2.918). This variability suggests differences in how strongly certain classes are suppressed.
-
-2. **Retain Class Accuracy and Loss**:
-   - Retain class accuracy is reasonably high across most classes, with "Cat" achieving the highest accuracy (86.50%) and "Deer" the lowest (72.38%). This shows that the model generally performs well on the retained classes.
-   - Retain class loss is minimal across all classes, with the lowest being "Cat" (0.423) and the highest being "Deer" (0.839). The low losses indicate stable performance on the retained classes.
-
-3. **Class-Specific Observations**:
-   - Some classes, such as "Ship" and "Horse," exhibit slightly higher forget class accuracies (0.6% and 0.3%, respectively), suggesting minor challenges in completely excluding these classes.
-   - The variability in retain class accuracy and forget class loss indicates that some classes are inherently harder to balance during the selective exclusion process.
-
----
-
-### Conclusion
-
-The results demonstrate the robustness of the **Selective Synapse Dampening** approach for excluding specific classes while maintaining high performance on the retained classes. However, certain trends and challenges emerge:
 
-- **Strengths**:
-  - The forget class accuracy is near zero for most classes, achieving effective exclusion of the target class.
-  - Retain class accuracy remains high (>70%) across all classes, with minimal loss, indicating strong performance on the retained tasks.
 
-- **Weaknesses**:
-  - Minor residual accuracy for excluded classes like "Ship" (0.6%) suggests room for improvement in class suppression.
-  - The retain class accuracy for "Deer" (72.38%) is noticeably lower compared to other classes, indicating potential trade-offs in performance for certain categories.
 
-- **Future Work**:
-  - Further refinement in suppression mechanisms could improve the forget class accuracy for challenging classes like "Ship" and "Horse."
-  - Adaptive techniques to balance performance across all retained classes may help address discrepancies in retain class accuracy (e.g., "Deer").
-  - A deeper analysis into class-specific characteristics could guide targeted optimization, making the model more robust for practical applications.