When I first started working with neural networks, I mostly cared about one thing: accuracy.
If a model performed well on the test set, I considered it a good model. But as I started looking beyond training and experimentation, I realized that accuracy is only part of the story.
A model can be accurate and still be too large, too slow, or too expensive to deploy.
That is what led me to explore model compression.
Why Do We Need Model Compression?
Modern deep learning models can contain millions or even billions of parameters. These parameters allow models to learn complex patterns, but they also come with a cost.
Larger models generally require:
- More memory
- More computation
- More storage
- More energy
- More powerful hardware
This becomes especially important when we want to run models on devices with limited resources.
Instead of always making models bigger, model compression asks a different question:
Can we make a model smaller without losing too much of what makes it useful?
That question introduced me to several interesting techniques for achieving model compression.
Some of the most well-known techniques include:
- Pruning
- Quantization
- Low-Rank Factorization
- Knowledge Distillation
- Weight Sharing
Each technique approaches model compression differently, but the goal is the same: reduce the model’s size and computational cost while preserving as much of its performance as possible.