Model Compression Vs Finetuning

Model Compression Techniques:

Model compression techniques are strategies used to reduce the size, latency, and computational requirements of machine learning models—especially deep learning models—while preserving accuracy. These techniques are crucial for deploying models on edge devices, mobile phones, or in production environments with strict performance constraints.

Common Model Compression Techniques

1. Pruning

Removes unnecessary weights or neurons from the model:

• Weight pruning: Set small-magnitude weights to zero.

• Structured pruning: Remove entire filters, channels, or layers for better hardware efficiency.

2. Quantization

Reduces the precision of the weights and activations:

• Post-training quantization: Convert a trained model (e.g., from float32 to int8).

• Quantization-aware training (QAT): Train with quantization simulated during training for higher accuracy.

3. Knowledge Distillation

A smaller "student" model learns to mimic the behavior of a larger "teacher" model:

• Helps retain performance by transferring learned knowledge.

4. Low-Rank Factorization

Decomposes large weight matrices into products of smaller matrices:

• Reduces number of parameters and computation.

5. Weight Sharing

Groups similar weights and shares the same value:

• Reduces model size with minimal impact on accuracy.

6. Compact Network Design

Use lightweight architectures from the start:

• Examples: MobileNet, SqueezeNet, EfficientNet.

7. Neural Architecture Search (NAS)

Automatically finds efficient architectures under constraints:

• Tools like Google's AutoML, FBNet.

8. Parameter Quantization and Huffman Coding

Used in models like Deep Compression:

• Combine quantization, pruning, and entropy coding for aggressive compression.

Model Fine Tuning 

Model fine-tuning is the process of taking a pretrained model and continuing its training on a new, often smaller or domain-specific dataset. It allows you to leverage existing knowledge (from a large general-purpose dataset) and adapt the model to a new task, reducing the need for large amounts of data and computation.

Common Fine-Tuning Techniques

1. Full Fine-Tuning

• Update all layers of the pretrained model using the new dataset.

• Most flexible but can lead to overfitting if data is small.

• Example: Fine-tuning all of BERT’s layers on a sentiment analysis dataset.

2. Feature Extraction (Partial Fine-Tuning)

• Freeze most of the pretrained layers and only train the final layers (e.g., classifier).

• Faster and prevents overfitting when data is limited.

• Common in CNNs (e.g., using ResNet as a feature extractor).

3. Layer-wise Freezing/Unfreezing

• Gradually unfreeze layers during training:

  • First, train the top layers.

  • Then slowly unfreeze lower layers for fine-grained tuning.

• Useful for transfer learning in NLP and CV.

4. Discriminative Learning Rates

• Use different learning rates for different layers:

  • Lower learning rate for early (frozen or pretrained) layers.

  • Higher learning rate for newly added or task-specific layers.

• Helps stabilize training and retain learned representations.

5. Adapter Layers (Parameter-Efficient Fine-Tuning)

• Add small bottleneck layers (adapters) to a frozen backbone and only train those adapters.

• Used widely in NLP (e.g., with BERT, T5) to minimize the number of trainable parameters.

6. Prompt Tuning / Prefix Tuning (NLP-specific)

• Add learnable prompts or prefixes to the input of a frozen language model.

• Efficient for large language models when full fine-tuning is too expensive.

7. Low-Rank Adaptation (LoRA)

• Introduce low-rank matrices into pretrained layers and train only those.

• Reduces memory and computation while achieving competitive performance.

When to Use Fine-Tuning

• When you have a small or domain-specific dataset.

• When pretraining from scratch is too resource-intensive.

• When model performance on your specific task needs improvement.

Difference between model compression and finetuning

Model compression and fine-tuning serve different purposes in the machine learning lifecycle, though they can sometimes be used together. Here's a clear comparison:

Simple Analogy:

• Model compression is like zipping a file to make it easier to store or send.

• Fine-tuning is like editing a document to make it more relevant for a specific audience.

Feature	Model Compression	Fine-tuning
Goal	Reduce model size, latency, or resource usage	Improve or adapt model performance on a new or specific dataset
Main Focus	Efficiency (smaller, faster models)	Accuracy and adaptation (often domain-specific)
Techniques Used	Pruning, quantization, distillation, low-rank factorization	Continue training on new data (often with smaller learning rate)
Changes to Architecture	Often modifies or simplifies architecture	Typically keeps the same architecture
When Used	After training a large model to optimize for deployment	When transferring a pretrained model to a new task or dataset
Impact on Accuracy	May cause slight degradation (ideally minimal)	Often improves accuracy on the new task
Example Use Case	Deploying a model on mobile or embedded systems	Adapting BERT for a legal text classification task