## 4 Experimental Results and Discussion
### ImageNet Dataset  
ImageNet is a widely used dataset consisting of 1.2 million RGB images of 224 \(\times\) 224 pixels for training and 50,000 for testing [84]. **Architecture**: We tested our method using two different architectures, namely AlexNet and ResNet-18 [3, 71]. **Training Process**: For the ImageNet dataset, we used four NVIDIA Tesla V100 GPUs. The models were trained using pre-trained models from the PyTorch Torchvision library [81]. The models were decomposed based on the layer sensitivity method and fine-tuned for 20-25 epochs using the Adam optimizer with a learning rate of 1 \(\times\) 10\({}^{-4}\) and weight decay of 1 \(\times\) 10\({}^{-7}\). The binarized models were trained from scratch for 70 epochs using the Adam optimizer with a learning rate of 0.01 and weight decay of 1 \(\times\) 10\({}^{-4}\). The ReduceLROnPlateau scheduler was used with a patience of 10 to reduce the learning rate by a factor of 0.005. Finally, the models were further fine-tuned with the AdamW optimizer for an additional 10-15 epochs. **Results**: The accuracy and model size after decomposition and binarization are shown in Table 7.  
**Discussion**: Table 7 presents a comparison of various network architectures on the ImageNet dataset, showcasing the accuracy (Top-1 and Top-5), parameter size, and compression ratios for the full-precision (FP) models, tensorized models, binary neural network (BNN) models, and our proposed method. **AlexNet**: The FP model achieves a top-1 accuracy of 56.66% and top-5 accuracy of 79.09% with a large parameter size of 244 MB. The FP-Tensorized version significantly reduces the model size to 28.32 MB, achieving an 8.61\(\times\) compression ratio, although the accuracy slightly decreases to 54.24% for top-1 and 76.83% for top-5. The BNN Model, using 1-bit quantization, achieves a more efficient compression with a model size of 22.83 MB \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & _Top-1 (\%)/Top-5 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline \multicolumn{4}{c}{AlexNet} \\ \hline FP Model & 56.66\%/79.09\% & 244 & – \\ FP-Tensorized & 54.24\%/76.83\% & 28.32 & 8.61\(\times\) \\ BNN Model & 46.69\%/70.21\% & 22.83 & 10.68\(\times\) \\ Ours & 44.25\%/69.78\% & 15.9 & 15.3\(\times\) \\ \hline \multicolumn{4}{c}{Resnet-18} \\ \hline FP Model & 69.75\%/89.08\% & 46.8 & - \\ FP-Tensorized & 66.31\%/86.21\% & 5.84 & 8.01\(\times\) \\ BNN Model & 52.16\%/72.24\% & 4.01 & 11.67\(\times\) \\ Ours & 50.06\%/70.14\% & 2.64 & 19.02\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of different architectures on ImageNet. and a 10.68\(\times\) compression ratio, but this comes with a more noticeable drop in accuracy, reaching 46.69% for top-1 and 70.21% for top-5. Our proposed method improves the compression ratio, reducing the model size to 15.9 MB with a 15.3\(\times\) compression ratio. Although the accuracy decreases to 44.25% for top-1 and 69.78% for top-5, the high compression achieved by our method makes it particularly advantageous for edge computing environments where storage and computational efficiency are critical. **ResNet-18**: The FP Model achieves 69.75% top-1 accuracy and 89.08% top-5 accuracy with a model size of 46.8 MB. The FP-Tensorized version compresses the model to 5.84 MB, achieving an 8.01\(\times\) compression ratio while maintaining respectable accuracy, at 66.31%, for top-1 and 86.21% for top-5. The BNN model, using binary quantization, reduces the model size to 4.01 MB with an 11.67\(\times\) compression ratio, although the top-1 accuracy drops to 52.16% and the top-5 drops to **72.24%. Our proposed method achieves the highest compression ratio among the models, reducing the parameter size to 2.64 MB with a 19.02x compression ratio. While the top-1 accuracy decreases to 50.06% and the top-5 accuracy decreases to **70.14%, the trade-off between accuracy and compression makes our approach highly efficient for deployment on edge devices where memory constraints are critical.