# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 2 Related Works
### Pruning  
Pruning techniques have significantly transformed the field of deep neural networks by offering methods for compressing models, improving resource efficiency, and enablingefficient inference without sacrificing performance. These techniques encompass both structured and unstructured pruning approaches, each with their own unique contributions. Structured pruning involves removing entire structures or components from the network, such as filters or channels, while unstructured pruning focuses on removing individual weights. Magnitude pruning, a popular form of unstructured pruning, identifies and prunes weights with low magnitudes, resulting in sparse networks with reduced parameters. L1 channel pruning, on the other hand, falls under the category of structured pruning. It removes entire channels from convolutional layers based on the L1 norm of the channel's weights. This approach has gained attention for its ability to reduce model size and computational requirements while maintaining performance. Unstructured pruning involves the elimination of individual weights, yielding sparse networks with reduced parameters. A key technique within this domain is magnitude pruning, which focuses on removing low-magnitude weights. Magnitude pruning can be enhanced by incorporating L1 norm regularization, which emphasizes weight sparsity. [PERSON] and [PERSON] (2019) introduced deep compression, a pioneering framework that seamlessly integrates magnitude pruning with quantization. This approach efficiently compresses models by pruning unimportant weights. Furthermore, [PERSON] et al. (2019) introduced the concept of \"Learning Both Weights and Connections\", extending unstructured pruning to include connection removal, demonstrating the synergy between structured and unstructured pruning. Structured pruning techniques maintain the network's architecture while reducing complexity. L1 channel pruning, a standout method, involves removing entire channels based on the L1 norm of weights, ensuring the preservation of critical information. [PERSON] et al. (2019) proposed structured pruning with proximal operators, a technique combining magnitude pruning and structured sparsity. This method optimizes model performance retention during pruning. Additionally, [PERSON] et al. (2019) introduced adaptive channel widening, a novel approach that blends magnitude pruning with dynamic channel operations to adaptively adjust channel widths. Recent advancements have showcased the efficacy of combining structured and unstructured pruning techniques for enhanced model compression. [PERSON] et al. (2019) introduced an efficient inference engine for compressed neural networks, incorporating L1-norm-based pruning and quantization. [PERSON] et al. (2019) introduced approximated oracle filter pruning, which combines magnitude pruning with filter-level optimization for width reduction. The concept of \"Deep Compression\" by [PERSON] et al. (2019) further demonstrates the efficacy of integrating structured and unstructured pruning, quantization, and Huffman coding in a comprehensive approach. Pruning alone does not provide precise control over the amount of compression, making it challenging to find the right balance between compression and performance. However, combining pruning with knowledge distillation (KD) addresses this limitation and offers a more controlled compression strategy. By incorporating KD alongside pruning, our approach overcomes the challenges of finding an optimal compression level. KD allows us to leverage the distilled knowledge of a teacher model to guide the pruning process. This eliminates the need for extensive adjustments and fine-tunes only the last connected layer. As a result, we achieve a more compressed network that outperforms conventional pruning techniques. The advantage of our method lies in its ability to intelligently combine knowledge distillation and pruning, resulting in greater compression. By leveraging the teacher model's knowledge, we achieve a more accurate and balanced compression strategy.