Neural networks have revolutionized many fields, including image reconstruction. However, these networks are often large and complex, which can make them difficult to use in practical applications. To address this issue, researchers have proposed a technique called "deep compression," which reduces the size of neural networks while preserving their performance. In this article, we explore how deep compression works and its potential applications.
Deep Compression Techniques
The authors propose two main techniques for deep compression: (1) encoding time-varying data using multiple small MLPs and (2) quantization and entropy encoding. The first technique arranges similar blocks in the same MLP, allowing each MLP to handle blocks with different content levels. This balances the workload during training, enabling more aggressive pruning without quality degradation. The second technique involves quantizing MLPs’ weights, biases, and shared parameters while fine-tuning the corresponding shared parameters through backpropagation. This technique reduces the number of parameters while maintaining performance accuracy.
Entropy Encoding
The final step in deep compression is entropy encoding, which compresses the resulting model file further using Huffman coding. Empirically, entropy encoding brings an additional 10% model size reduction.
Applications and Future Work
Deep compression has many potential applications, including image reconstruction, computer vision, and machine learning. In the field of image reconstruction, deep compression can be used to reduce the size of neural networks without compromising their performance. This can lead to faster inference times and reduced computational costs, making it easier to use these networks in practical applications. Future work involves exploring other techniques for deep compression, such as incorporating attention mechanisms or using different quantization schemes.
Conclusion
Deep compression is a promising technique that can significantly reduce the size of neural networks while preserving their performance. By leveraging the sparsity of neural networks and using entropy encoding, researchers can compress these networks without compromising their accuracy. As deep learning continues to evolve, techniques like deep compression will become increasingly important for practical applications.
