During the past few years, our perspective on artificial intelligence (AI) for performing different tasks has been changed by deep learning models. However, these models are very bulky and complicated, making it difficult to use them on devices with limited resources, like smartphones. Knowledge distillation is a technique that addresses this issue and is used to reduce the complexity and size of a model without affecting its accuracy and efficiency.
Knowledge Distillation Meaning
Knowledge distillation is the process of creating and training a smaller model(i.e. student) that mimics the behavior and performance of the large and more complex model (i.e. teacher) by transferring knowledge from the teacher to the student model in machine learning. It is essential for reducing the size and complexity of large language models (LLMs) while maintaining accuracy and effectiveness.
Knowledge distillation focuses on reducing the memory footprint and computational requirements of a model without significantly affecting or degrading the performance.
This technique is essential in TinyML (machine learning models on tiny devices) applications, where model size and computational complexity are important components. Knowledge distillation creates new possibilities in TinyML and model optimization for any application field by reducing the size of the deep learning models.
Knowledge distillation was first introduced by Hinton et al. in 2015. Since then, knowledge distillation techniques have been successfully used across various fields, including natural language processing(NLP), speech and image recognition, and object detection.
In recent years, the study of knowledge distillation has been of particular importance to large language models. For LLMs, it has emerged as an effective means of transferring advanced capabilities from leading proprietary models to smaller, more accessible open-source models.
How Does Knowledge Distillation Work?
The main concept of knowledge distillation includes encouraging the student model to learn from the teacher model, beyond the raw data sets to generate soft targets.
These soft targets are then used to train the student model, minimizing the difference between the teacher model’s generated soft targets and the student model’s predicted outputs.
The Steps involved in knowledge Distillation are:
1. Training The Teacher Model: the process begins by training the large and complex teacher model on a dataset using standard training procedures. The teacher model has a wide range of parameters, making it capable of learning complex data.
2. Generating Soft Targets: once a teacher model is trained, it generates predicted probabilities for each class, known as soft targets. These soft targets contain more information than hard targets and capture the uncertainties in the predictions of the teacher model.
3. Training The Student Model: After the generation of soft targets, these targets are used to train smaller networks, called student models. It is trained to minimize the difference between its predictions and the soft targets generated by the teacher model, thus improving its ability to generalize the output.
4. Fine-Tuning: the student model is then fine-tuned in some cases, to increase the accuracy and efficiency of the model.
Types of knowledge Distillation Techniques
Three types of knowledge distillation techniques are commonly used:
1. Offline Distillation: In this technique, the pre-trained teacher model remains frozen while the student model is trained. The teacher model is given less attention and focuses on improving the knowledge transfer mechanism. The probability distribution of data for knowledge transfer enables cross-modal knowledge transfer and the transfer of knowledge from handcrafted feature extractors into neural networks.
2. Online distillation: a large pre-trained teacher model is not available in this technique, rather, the teacher and student models are trained simultaneously. It involves mutual training, fusing the sub-model features, and allowing efficient knowledge transfer.
3. Self-Distillation: it offers a solution to the challenge related to the selection of the teacher model and the potential accuracy decline in the student models during inference. In this technique, the same model acts as both, the teacher and student.
Advantages of Knowledge Distillation
1. The most significant advantage of knowledge distillation is that it reduces the memory and computational requirements of a model, all the while maintaining its efficiency similar to that of the larger models. These models can then be used on devices with limited resources like mobile phones.
2. The smaller devices that knowledge distillation is used on, require less computational power resulting in output generation in much less time. This advantage is useful in real-life applications like driving, medical imaging, etc.
3. Knowledge distillation leads to better generalization of data by the student model as it learns from the soft targets created by the teacher model. It results in improved performance and efficiency on the input data.
4. Needing less energy to function, the smaller models become more sustainable in resource-constrained situations.
Uses of Knowledge Distillation
1. With the increasing dependence on LLMs, knowledge distillation helps transfer the capabilities of the larger and costlier language models to smaller, more compatible ones. It also trains LLMs to be used for specific cases based on the requirements.
2. Knowledge distillation helps make LLMs multilingual by using separate teacher models specialized in different languages to train a single student model.
3. It also helps to improve the accuracy and efficiency of the student model by ranking the generated output based on human feedback.
4. Knowledge distillation also helps with processing data in real-time without a cloud connection for applications like voice assistants, mobile cameras, etc.
5. It can also be used in the healthcare sector, requiring real-time processing for medical imaging and diagnosis by creating lightweight models that predict quicker results.
Limitations of Knowledge Distillation
Knowledge distillation, though having multiple advantages, has its limitations and challenges as well.
1. Valuable information can be lost while reducing the size and complexity of the models, thus affecting the accuracy.
2. Selecting the appropriate teacher model to train the student model is crucial for the performance of the student model.
3. Training the student model can be a tedious, computationally expensive task.
Conclusion
Knowledge distillation has become an important task in optimizing machine learning models, making them smaller, simpler, and energy-efficient, without reducing their accuracy and performance. It is done by using soft targets generated by larger models to train the smaller models, allowing them to be used across various types of devices. It helps bridge the gap between larger, more complex models and smaller, resource-limited devices.
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Frequently Asked Questions
Knowledge distillation is a technique used to train smaller models, called student models, by transferring the knowledge and data from large and complex teacher models to make the models more accessible for devices with limited resource availability. The student model learns to mimic the output generation of the teacher model at a much lower cost.
The main goal of knowledge distillation is to reduce the memory footprint and computational requirements of a model without significantly affecting or degrading the performance. This technique is essential in TinyML (machine learning models on tiny devices) applications, where model size and computational complexity are vital considerations.
Knowledge distillation works with the teacher model generating soft targets, the probable outputs and predictions, and not just the labeled data. These soft targets are then used to train the student model, resulting in the improved generalization of the model.
There are three basic techniques used for knowledge distillation: offline, online, and self-distillation. For offline distillation, the pre-trained teacher model remains frozen while the student model is being trained, whereas, in online distillation, a large, pre-trained teacher model is not available at all; rather, the teacher and student models are trained simultaneously. In self-distillation, the same model acts as teacher and student, offering a solution to the challenge of selecting a teacher model.
Knowledge distillation helps reduce the memory and computational requirements of a model while maintaining its efficiency similar to that of the larger models. These models can then be used on devices with limited resources, like mobile phones, and can generate quicker results with low energy consumption . It also results in better generalization of the data by smaller models, improving their accuracy and efficiency.