Modern AI systems often rely on large, high-capacity “teacher” models that deliver strong accuracy but are expensive to run. In real deployments, teams frequently need smaller “student” models that are faster, cheaper, and easier to serve on limited hardware. Knowledge distillation is a training approach that bridges this gap. Instead of learning only from hard labels (the correct class), the student also learns from the teacher’s output probabilities—called soft targets—which contain richer information about how the teacher “sees” each example. If you are building practical machine learning skills through an AI course in Kolkata, distillation is a useful concept because it connects model performance with real-world constraints like latency and cost.
What “Soft Targets” Actually Mean
In standard supervised learning, a classifier is trained using one-hot labels: the correct class is 1, all others are 0. This teaches the model what is correct, but it hides relationships between classes. Soft targets are different. The teacher outputs a probability distribution across classes, such as:
- Cat: 0.72
- Fox: 0.18
- Dog: 0.07
- Others: 0.03
Even though “Cat” is the top class, the probabilities show that the teacher considers “Fox” somewhat similar. This is valuable signal for the student, especially when training data is limited or noisy. Soft targets can be made even more informative by applying a temperature parameter to the teacher’s logits. Higher temperature “softens” the distribution, increasing smaller probabilities and revealing more structure in the teacher’s knowledge.
How Distillation Training Works
Knowledge distillation typically uses a combined loss that mixes two learning signals:
- Hard-label loss: The student learns from ground-truth labels (usually cross-entropy).
- Soft-target loss: The student matches the teacher’s softened probability distribution (often using KL divergence).
A common setup looks like this:
- Train or select a strong teacher model.
- For each training sample, compute the teacher’s probabilities (with temperature).
- Train the student to both predict the correct label and imitate the teacher’s distribution.
This dual objective acts as a regulariser. The student does not only memorise “right vs wrong,” but learns the teacher’s decision boundaries and inter-class similarities. In practice, this can improve generalisation and reduce overconfidence. Many learners in an AI course in Kolkata encounter this as a key technique for model compression and production readiness.
Why Soft-Target Distillation Improves a Smaller Model
Soft-target training helps the student in several ways:
Richer supervision than labels alone
Hard labels provide a single bit of information: correct or incorrect. Soft targets provide a full distribution, which can encode subtle cues, such as “this image is mostly a cat, but somewhat like a fox.” This helps the student learn smoother, more informative representations.
Reduced overfitting
When datasets are small or biased, a student trained only on labels may overfit. Soft targets encourage the student to follow the teacher’s calibrated probabilities, which often leads to more stable learning and better generalisation.
Better decision boundaries
Soft targets can guide the student in ambiguous regions of the feature space. Instead of pushing the student to output 1.0 for the correct class and 0.0 for the rest, distillation encourages a more nuanced boundary that mirrors the teacher.
Deployment advantages
Distillation is often chosen because the student is lightweight. That means lower inference latency, reduced memory use, and better throughput—critical for applications like real-time recommendations, edge devices, and high-traffic APIs.
Practical Steps and Design Choices
To apply distillation effectively, teams usually make a few key decisions:
- Teacher quality matters: A weak teacher can mislead the student. A reliable teacher typically improves student outcomes more consistently.
- Temperature selection: If temperature is too low, soft targets resemble hard labels. If too high, the distribution becomes too flat. Teams often tune temperature using validation performance.
- Balance between losses: The weighting between hard-label loss and soft-target loss affects behaviour. Too much emphasis on the teacher can cause the student to copy teacher mistakes; too little reduces the benefit of distillation.
- Same task vs transfer: Distillation can be used for the same classification task, or in a more advanced setting where a teacher transfers knowledge across tasks or domains.
These choices are not purely theoretical. They directly influence accuracy, calibration, and the model’s reliability under data shift—topics that often come up in hands-on labs in an AI course in Kolkata.
Common Pitfalls to Avoid
Even though distillation is conceptually simple, a few mistakes are common:
- Copying the teacher’s biases: The student can inherit teacher errors and biases. Monitoring fairness, robustness, and failure cases still matters.
- Mismatch in input pipelines: If teacher and student see different preprocessing or tokenisation, the student may struggle to learn the teacher’s outputs consistently.
- Ignoring calibration: A teacher can be accurate but poorly calibrated. Distillation from an overconfident teacher may reduce student reliability unless you address calibration.
- Evaluating only top-line accuracy: Distillation often improves not just accuracy but also latency and cost. Always measure end-to-end outcomes: speed, memory footprint, and error patterns.
Conclusion
Knowledge distillation with soft targets trains a smaller student model by leveraging the probability outputs of a stronger teacher model. By learning from both ground-truth labels and softened teacher distributions, the student can generalise better, learn smoother decision boundaries, and deliver stronger performance under tight deployment constraints. For anyone aiming to build production-ready ML skills—especially through an AI course in Kolkata—soft-target distillation is a practical technique that connects model theory to the realities of speed, scale, and cost.
