Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
Valerie is Techopedia's Editor-in-Chief. She is a skilled writer and editor with expertise in crafting evergreens, analyses, forecasts, and educational materials, covering global financial markets,…
Knowledge distillation (KD) is a machine learning (ML) compression process that transfers knowledge from a large deep learning (DL) model to a smaller, more efficient model. In this context, knowledge refers to the patterns and behavior that the original model learned during training.
The goal of knowledge distillation is to reduce the memory footprint, compute requirements, and energy costs of a large model, so it can be used in a resource-constrained environment without significantly sacrificing performance.
Knowledge distillation is an effective way to improve the accuracy of a relatively small ML model. The process itself is sometimes referred to as teacher/student learning. The large model is the teacher, and the small model is the student.
During the distillation process, the teacher model (a large, pre-trained foundation model) generates soft labels on the training data. The soft labels, which are essentially output probability distributions, are then used to train the student model.
Here is a very simple example of how knowledge distillation could be used to train a student model:
If the performance level of the student model is acceptable, the student model can be deployed. If the performance level of the student model is unacceptable, the student model can be retrained with additional data or optimized by adjusting hyperparameters, learning rates, and/or the distillation temperature.
A higher temperature makes the probability distributions softer (less peaky), while a lower temperature makes it sharper (more peaky and closer to the hard labels).
Attention transfer is a technique in which the student model is trained to mimic the attention maps generated by the teacher model. Attention maps highlight the important regions in an image or a sequence of words.
FitNets is a technique in which the student model is trained to match the intermediate representations of the teacher model. Intermediate representations are the hidden layers of the model that capture the underlying features of the input data.
In this technique, the student model is trained to match the similarity matrix of the teacher model. The similarity matrix measures the pairwise similarities between multiple input samples.
Hint-based distillation is a technique in which the student model is trained to predict the difference between the outputs of the teacher model and the student model. This difference is called the hint.
The student model is trained using a loss function that combines the standard classification loss with a distillation loss that measures the difference between the teacher and student model’s output probabilities.
Knowledge distillation is an important technique for creating lightweight machine-learning models. These distilled models are especially beneficial for recommendation systems and IoT edge devices that have computational constraints.
By using knowledge distillation, devices like security cameras, smart home systems, and virtual digital assistants can perform a wide range of complex tasks locally, including:
One of the main advantages of knowledge distillation is that it enables the creation of smaller and faster models that perform well on Internet of Things (IoT) edge devices. It should be noted, however, that knowledge distillation often involves dealing with a trade-off between size and an acceptable level of accuracy.
One of the biggest challenges with developing business-to-consumer (B2C) applications that use artificial intelligence (AI) is that edge computing devices like mobile phones and tablets have limited storage and processing capabilities.
That leaves machine learning engineers with one option if they want to run a large model on an edge device: reduce the size of the model with compression techniques such as neural network pruning, quantization, low-rank factorization, and knowledge distillation.
Knowledge distillation is sometimes referred to as a type of transfer learning, but the two concepts have different purposes.
The goal of knowledge distillation is to create a smaller machine-learning model that can solve the same task as a larger model.
In contrast, the goal of transfer learning is to reduce the time it takes to train a large model to solve a new task by using knowledge gained from a previously learned task.
Data distillation and knowledge distillation are both compression processes, but they target different components. Data distillation focuses on the training data itself. Its goal is to obtain a smaller subset of the data that still represents the original large dataset.
In contrast, knowledge distillation focuses on reducing a model’s size without losing efficiency and accuracy.
Techopedia’s editorial policy is centered on delivering thoroughly researched, accurate, and unbiased content. We uphold strict sourcing standards, and each page undergoes diligent review by our team of top technology experts and seasoned editors. This process ensures the integrity, relevance, and value of our content for our readers.
Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
What is Turnitin AI Checker? The Turnitin AI checker is an advanced tool aimed at maintaining the integrity of school...
Maria WebbTechnology journalist
What is ISO/IEC 42001? ISO/IEC 42001 is an international standard that provides a governance framework for implementing and continually improving...
Margaret RouseTechnology Expert
What are Physical Resource Networks (PRNs)? The definition of Physical Resource Networks (PRNs) is that they are a type of...
Nicole WillingTechnology Journalist
Trending NewsLatest GuidesReviewsTerm of the Day