How do I apply lossless compression techniques to reduce the size of deep learning models

Question

Can i know How do I apply lossless compression techniques to reduce the size of deep learning models?

Answer 1

To apply lossless compression and reduce deep learning model size, use weight pruning, quantization (post-training/static), Huffman coding, knowledge distillation, and ZIP/GZIP compression, ensuring no accuracy loss.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Weight Pruning (tfmot.sparsity.keras):

  • Removes unnecessary weights without affecting model accuracy.

• Post-Training Quantization (TFLite):

  • Converts model weights to lower precision (e.g., 16-bit, 8-bit) while preserving accuracy.

• Huffman Coding for Weight Storage:

  • Uses entropy-based compression to reduce redundant bit storage in model files.

• Knowledge Distillation (KD):

  • Transfers knowledge from a large model to a smaller model without accuracy loss.

• ZIP/GZIP Model Compression:

  • Uses standard compression (gzip, bzip2, lzma) for model storage reduction.

Hence, applying weight pruning, quantization, Huffman coding, and distillation effectively reduces deep learning model size while maintaining accuracy, making deployment more efficient.