Distillation in AI | Squareroots

Unlocking the Power of Knowledge Distillation in AI: Lessons from a High-Speed Quality Inspection Project

As AI models grow in complexity, so does the challenge of deploying them efficiently in real-world applications. Large models like GPT, BERT, and ViTs (Vision Transformers) deliver state-of-the-art performance but often come with high computational costs. How can we retain their intelligence while making them faster and more efficient?

My Experience: AI-Powered High-Speed Quality Inspection

In one of my recent projects, we developed an AI-powered high-speed quality inspection system for a manufacturing setup where both accuracy and speed were critical. The system needed to process thousands of products per minute, identifying defects with high precision while maintaining real-time performance. Initially, we deployed a large deep learning model to ensure maximum accuracy. However, we quickly faced challenges related to inference speed and computational cost.

The large model, while highly accurate, required significant computational resources and struggled to keep up with the real-time processing demands. Running inference on every product in a high-speed production line resulted in latency issues, making it impractical for deployment. We needed a way to maintain high detection accuracy while significantly improving inference speed and reducing computational overhead.

Approach Analysis: Evaluating Model Compression Techniques

To address this challenge, we explored various model compression techniques to find the best approach. Below is a Pugh Matrix that helped us assess different techniques based on key factors such as accuracy retention, inference speed, model size reduction, ease of implementation, and computational cost:

** The stars in the Pugh Matrix were assigned based on a qualitative assessment of each approach’s strengths and weaknesses in relation to our project needs.

Selecting the Optimal Approach

Quantization showed great potential in reducing memory and increasing inference speed, but it introduced noticeable accuracy degradation, which was a deal breaker for our quality inspection system.
Pruning helped reduce model size while keeping the structure intact, but it required extensive fine-tuning and still left some computational inefficiency.
Neural Architecture Search (NAS) generated highly optimized models, but the computational cost was too high, making it impractical for our rapid deployment needs.
Early Exit Mechanisms provided an interesting approach to reducing computational overhead, but we needed a more robust solution that maintained consistent accuracy across all cases.

After thorough evaluation, Knowledge Distillation emerged as the most suitable technique for ensuring high-speed, accurate quality inspection while maintaining computational efficiency. It provided an optimal trade-off between model size, accuracy, and inference speed. By transferring knowledge from a complex teacher model to a smaller student model, we achieved 2x faster processing speed while maintaining near-teacher accuracy, making real-time inspection feasible for our client.

What is Knowledge Distillation?

Knowledge Distillation is a model compression technique where a smaller, lightweight model (student) learns to replicate the behaviour of a larger, more complex model (teacher). Unlike traditional training methods that rely solely on labelled data, KD enables the student model to learn from the teacher’s probability distributions, capturing nuanced patterns and generalization abilities

How It Works

The process typically involves three key steps:

Teacher Model Training – A large neural network is trained on a dataset to achieve high accuracy.
Soft Label Generation – The teacher produces probability distributions (soft labels) instead of just hard class labels. These soft labels contain richer information about class relationships.
Student Model Training – The student learns using a combination of the traditional loss (e.g., cross-entropy) and a distillation loss, which helps mimic the teacher’s behaviour.

One important technique in KD is temperature scaling, where the teacher’s output logits are smoothed to emphasize similarities between classes, making it easier for the student to learn meaningful patterns.

Why Knowledge Distillation Mattered in Our Project

Model Compression – The distilled model required significantly fewer computational resources, allowing us to deploy it on edge devices in the factory without reliance on expensive cloud infrastructure.
Faster Inference – By reducing model complexity, we achieved real-time processing, ensuring that defective products were identified instantly, and preventing production bottlenecks.
Efficient Knowledge Transfer – The student model retained the intelligence of the teacher model, enabling it to generalize well to new defect patterns with minimal retraining.

Challenges with Accuracy and Explainability

While Knowledge Distillation offered significant improvements in speed and efficiency, transitioning from the teacher to the student model came with its own set of challenges:

Accuracy Trade-offs – The distilled student model, while efficient, initially struggled to match the accuracy of the teacher model, especially for edge cases and rare defect patterns. We had to fine-tune hyperparameters and experiment with different distillation loss functions to minimize performance gaps.
Explainability Issues – The student model’s decision-making process became less interpretable compared to the original teacher model. Since the teacher model had a larger number of parameters, it captured more complex feature representations, making its predictions easier to analyse. To mitigate this, we incorporated attention maps and feature attribution techniques to improve transparency and validate the student model’s reasoning.

Final Thoughts

My experience with Knowledge Distillation in high-speed quality inspection reinforced its immense value in real-world AI deployments. While the benefits are clear, addressing challenges related to accuracy and Explainability is crucial to maximizing its impact. As AI adoption grows, the demand for efficient, scalable, and cost-effective models will only increase. Whether you are building AI for enterprise applications or edge computing, KD can help unlock new possibilities.

Have you faced similar challenges in AI deployment? Let’s discuss your experiences!

Author: Greenu Sharma with AI assistance