Knowledge Distillation
Knowledge distillation is a technique where a smaller model (student) is trained to mimic the outputs of a larger model (teacher). This transfers the teacher's knowledge into a more efficient model suitable for deployment.
Understanding Knowledge Distillation
Knowledge distillation is a model compression technique where a smaller "student" model is trained to replicate the behavior of a larger, more capable "teacher" model. Rather than training on hard labels alone, the student learns from the teacher's soft probability distributions, which contain richer information about relationships between classes and uncertainty. This process transfers the teacher's learned knowledge into a compact model suitable for deployment on edge devices, mobile phones, or latency-sensitive applications where a large language model or deep neural network would be impractical. Knowledge distillation has been used to create efficient versions of BERT, GPT, and vision models, achieving much of the teacher's accuracy at a fraction of the computational cost. The technique is a key component of MLOps pipelines that need to balance inference speed, model size, and performance in production environments.
Category
Deep Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Deep Learning Terms
Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.