## 4 Experimental Results and Discussion
### CIFAR-100 Dataset  
CIFAR-100 differs from CIFAR-10 only in the number of classes, containing 100 classes instead of 10. We applied the same data augmentation techniques as used for CIFAR-10.  
**Architecture**: For CIFAR-100, we tested our method using two different architectures, namely ResNet-20 and ResNet-32 [71].  
**Training Process**: Similar to CIFAR-10, the models were trained on one NVIDIA GeForce GTX 1080 Ti GPU with a batch size of 32 for 320 epochs. The Adam optimizer and ReduceLROnPlateau scheduler were used as described earlier. After decomposition using the layer sensitivity method, we fine-tuned the models for 25-50 epochs with AdamW. Binarization was performed using the XNOR-net method, and the models were further fine-tuned as described previously.  
**Results**: The accuracy and model size after decomposition and binarization are presented in Table 6.  
**Discussion**: Table 6 presents a performance comparison of various neural network architectures applied to the CIFAR-100 dataset, highlighting the accuracy, parameter size, and compression ratio for full-precision (FP) models, tensorized models, and our proposed method.  
**ResNet-20**: The FP Model achieves the highest accuracy of 68.73% with a parameter size of 1.2 MB. The FP-Tensorized version reduces the parameter size to 1 MB with a 1.2\(\times\) compression ratio, although the accuracy drops slightly to 65.89%. The BNN model, using 1-bit quantization, compresses the model to 0.069 MB, achieving a 17.4\(\times\) compression, but this results in a significant drop in accuracy to 50.17%. In contrast, our proposed method (Ours) achieves an even higher compression ratio of 30.0\(\times\), reducing the model size to 0.040 MB while maintaining an accuracy of 48.66%. Although there is an accuracy trade-off, our method excels in compression, making it highly efficient for scenarios where memory is a key constraint, such as edge devices.  
**ResNet-32**: The FP model achieves 70.12% accuracy with a model size of 2 MB. The FP-Tensorized version compresses the model to 1.3 MB, achieving a 1.5\(\times\) compression  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & _Top-1 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline \multicolumn{4}{c}{Resnet-20} \\ \hline FP Model & 68.730\% & 1.2 & - \\ FP-Tensorized & 65.89\% & 1 & 1.2\(\times\) \\ BNN Model & 50.17\% & 0.069 & 17.4\(\times\) \\ Ours & 48.66\% & 0.040 & 30.0\(\times\) \\ \multicolumn{4}{c}{Resnet-32} \\ \hline FP Model & 70.12\% & 2 & - \\ FP-Tensorized & 68.54\% & 1.3 & 1.5\(\times\) \\ BNN Model & 51.2\% & 0.093 & 21.5\(\times\) \\ Ours & 48.01\% & 0.076 & 26.3\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different architectures on CIFAR-100.  
with a slight accuracy drop to 68.54%. The BNN model further reduces the model size to 0.093 MB, achieving a 21.5\(\times\) compression, but this comes at the cost of reduced accuracy, at 51.2%. Our proposed method (Ours) surpasses the BNN model in terms of compression, achieving a 26.3\(\times\) compression ratio by reducing the model size to 0.076 MB. Despite the compression, the accuracy remains competitive, at 48.01%, balancing model efficiency and performance.  
Our proposed method demonstrates high compression efficiency, achieving up to 30.0\(\times\) and 26.3\(\times\) compression compared to the full-precision and tensorized models, respectively. While the accuracy of our method is lower than that of the full-precision models, the huge reduction in model size makes it particularly well-suited for edge computing environments where memory and computational resources are highly constrained. This balance between compression and performance showcases the adaptability of our approach in real-world applications.